Measuring electricity-related GHG emissions and the affordability of electricity in Malaysian low-cost housing: A case study of low-cost housing projects in Kuala Lumpur
Noor Suzaini Mohamed Zaid
A thesis in fulfilment of the requirements for the degree of
Doctor of Philosophy
School of Planning and Urban Development
Faculty of Built Environment
November 2013
THE UNiVERSITY OF NEW SOUTH WALES Thesis/.Oissertation Sheet
Surname or F!!mlly name: Mohamed Zaid
First name: Noor Suz.alnl Other name/s:
Abbreviation for degree as given in the University calendar: PhD
School: Planning and Urban Development Research Faculty: Faculty of Built Environment
Title: Measuring electricity-related GHG emissions and the affordability of electricity in Malaysian low-cost housing: A case study of low-cost housing projects in Kuala Lumpur
Abstract 350 words maximum:
Malaysia Is yet to establish any mandatory energy efficiency or. energy performance building code. In the absence of such legislation, the Malaysian residential sector is likely loclling-in inefficiency for decades Into the future. This research focuses on the public low-cost housing typology (PPR), as the least environmentally researched typology, and has high potential for policy improvements as it is nationally administrated and is a required provision for all new housing developments. A project-specific baseline was developed for the first time in Malaysia using the the UNEP.SCI's Common Carbon Metric (CCM). The CCM tool provides an Internationally comparable baseline that Is consistent with the measurable, reportable and verifiable (MRV) framework for GHG emisssions reporting,. therefore was adopted for this research.
A case study was conducted to Investigate energy performance and greenhouse gas (GHG) emissions from building operation of two PPR in Kuala Lumpur. The CCM converted utilized electricity bills collected from the National Energy Provider Company (Tenaga Nasional Berhad) and presented GHG emissions of the sample (383 units) us·ing a bottom-up approach, and the PPR 'building stock' in Kuala Lumpur (27,102 units) using a top-down approach. Findings suggest that the average household's electricity consumption was relatively higher than benchmarks set by World Energy Council and the International Energy Agency. The importance of this research lies in generating a measured baseline of electricity consumption and GHG emissions of low-cost urban housing In Malaysia.
This research also provided an Indicative report on the affordabllity of electricity for low-income households, as Malaysian low-cost housing Is defined solely on sale price. and/or monthly rental. Affordabllity of electricity is important due to Issues such as energy poverty, access to energy and the rebound effecl Therefore, Investigating percentage of monthly household income spent on electricity and other utilities helps measure operational and long term affordablllty of PPRs. A survey questionnaire was conducted in two PPR to Investigate operational costs of tent, electricity and water, and to measure end-use electricity consumption patterns in terms of average operating time of electrical appliances. Examining end-use electricity consumption patterns was helpful In identifying the typology's energy profile and determining its energy savings potential. The research findings determined which characteristics of the building design can be improved based on electricity consumption data for thermal comfort, lighting and appliances. This research presents for the first time building energy performance data for this typology that is consistent with measurable, reportable and verifiable requirements. Its focus on a developing country experiencing rapid urbanisation gives broader relevance to both research design and methodology, and recommendations for policy makers In Malaysia and South East Asia.
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I also authorise University Microfilms to e the 350 word abstract of my thesis in Dissertallon Abstracts International (this is applicable to doctoral theses only).
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Measuring Electricity-Related GHG Emissions and the Affordability of Electricity in Malaysian Low-Cost Nov 2013 Housing: A Case Study of Low-Cost Housing Projects in Kuala Lumpur
Abstract
Malaysia is yet to establish any mandatory energy efficiency or energy performance building code. In the absence of such legislation, the Malaysian residential sector is likely locking-in inefficiency for decades into the future. This research focuses on the public low- cost housing typology (PPR), as the least environmentally researched typology, and has high potential for policy improvements as it is nationally administrated and is a required provision for all new housing developments. A project-specific baseline was developed for the first time in Malaysia using the UNEP-SCI’s Common Carbon Metric (CCM). The CCM tool provides an internationally comparable baseline that is consistent with the measurable, reportable and verifiable (MRV) framework for GHG emisssions reporting, therefore was adopted for this research.
A case study was conducted to investigate energy performance and greenhouse gas (GHG) emissions from building operation of two PPR in Kuala Lumpur. The CCM converted utilized electricity bills collected from the National Energy Provider Company (Tenaga Nasional Berhad) and presented GHG emissions of the sample (383 units) using a bottom- up approach, and the PPR ‘building stock’ in Kuala Lumpur (27,102 units) using a top- down approach. Findings suggest that the average household’s electricity consumption was relatively higher than benchmarks set by World Energy Council and the International Energy Agency. The importance of this research lies in generating a measured baseline of electricity consumption and GHG emissions of low-cost urban housing in Malaysia.
This research also provided an indicative report on the affordability of electricity for low- income households, as Malaysian low-cost housing is defined solely on sale price and/or monthly rental. Affordability of electricity is important due to issues such as energy poverty, access to energy and the rebound effect. Therefore, investigating percentage of monthly household income spent on electricity and other utilities helps measure operational and long term affordability of PPRs. A survey questionnaire was conducted in two PPR to investigate operational costs of rent, electricity and water, and to measure end- use electricity consumption patterns in terms of average operating time of electrical appliances. Examining end-use electricity consumption patterns was helpful in identifying the typology’s energy profile and determining its energy savings potential. The research findings determined which characteristics of the building design can be improved based on electricity consumption data for thermal comfort, lighting and appliances. This research presents for the first time building energy performance data for this typology that is
i
Measuring Electricity-Related GHG Emissions and the Affordability of Electricity in Malaysian Low-Cost Nov 2013 Housing: A Case Study of Low-Cost Housing Projects in Kuala Lumpur
consistent with measurable, reportable and verifiable requirements. Its focus on a developing country experiencing rapid urbanisation gives broader relevance to both research design and methodology, and recommendations for policy makers in Malaysia and South East Asia.
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Measuring Electricity-Related GHG Emissions and the Affordability of Electricity in Malaysian Low-Cost Nov 2013 Housing: A Case Study of Low-Cost Housing Projects in Kuala Lumpur
Acknowledgements
I would like to express my thanks and gratitude to all the people who have supported me throughout this journey.
My deepest appreciation to Dr. Peter Graham for his thoughtful and valuable guidance. His constructive supervision and demand for precision has helped me remained focussed and motivated.
My thanks to Professor Deo Prasad, my second supervisor for his encouraging discussions. Big thanks also to the administrative staff and fellow researchers at UNSW for their continuous support. Special thanks go to the following personnel who authorized fieldwork investigation and provided critical data to this research: Ms. Siti Yohanis from City Hall of Kuala Lumpur for allowing this research to be conducted in the public PPR low-cost housing, and Mr. Roma Das from Tenaga Nasional Berhad for providing electricity data.
The residents at the PPR low-cost housing who have opened their homes to me, and telling me their stories. I will forever be grateful for this experience and take with me life lessons I did not expect.
Finally but not least, I want to express heartfelt gratitude to my family and friends. To my parents, their unfailing support and encouragement to challenge boundaries and explore new things, has led me here today. A special dedication to my mother who went with me every day interviewing households. Your strength gives me courage. To my partners in crime, throughout times of hardship, grief and joy, your encouraging support carried me through, your challenging discussions kept me motivated, your compassion kept me grounded.
This research was funded by the Malaysian Ministry of Higher Education, under the Academic Training Scheme Scholarship.
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Measuring Electricity-Related GHG Emissions and the Affordability of Electricity in Malaysian Low-Cost Nov 2013 Housing: A Case Study of Low-Cost Housing Projects in Kuala Lumpur
Abbreviations and Units
CCM Common Carbon Metric and Protocol CIS Construction Industry Standard (Malaysia)
CO2 Carbon dioxide
CO2e. Carbon dioxide equivalent EEBC Energy efficiency building codes EPU Economic Planning Unit, Prime Minister’s Department (Malaysia) GBI Green Building Index (Malaysia) Gt Giga tonnes GHG Greenhouse gases IEA International Energy Agency IPCC Intergovernmental Panel on Climate Change kWh kilowatt hour kgCO2e. kilogram of carbon dioxide equivalent MRV Measurable, reportable and verifiable MS 1525:2007 Malaysian Standard (Voluntary) Code of Practice on Energy Efficiency and Use of Renewable Energy for Non-residential Buildings PPR Program Perumahan Rakyat (National Economic Action Council (NEAC) - People’s Housing Programme) RM Ringgit Malaysia SEA South East Asia TNB Tenaga Nasional Berhad (National Energy Provider Company) UBBL Uniform Building By-Laws (Malaysia) UN United Nations UNFCCC United Nations Framework Convention on Climate Change UNDP United National Development Programme UNEP United Nations Environment Programme UNEP-SBCI United Nations Environment Programme, Sustainable Building Climate Initiative
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Measuring Electricity-Related GHG Emissions and the Affordability of Electricity in Malaysian Low-Cost Nov 2013 Housing: A Case Study of Low-Cost Housing Projects in Kuala Lumpur
List of Figures
Figure 1.1 Percentage Change in Carbon Emissions from Fossil Fuel Use (1990-2005) ...... 3 Figure 1.2 Carbon Emissions in Metric Tons per Capita (2003-2010) ...... 4
Figure 1.3 BAU Forecast of Annual Energy Consumption and CO2 Emissions for Malaysian Building Sector ...... 20 Figure 1.4 Energy Consumption Assessment as Compared to 2005 Values using Three Different Scenarios ...... 26 Figure 1.5 Multiple Sources of Evidence ...... 35 Figure 1.6 Thesis Structure ...... 39
Figure 2.1 Economic mitigation potential by sector in 2030 estimated from bottom-up studies ...... 55 Figure 2.2 Global Final Energy Demand Projections by Various Models (between 2005 to 2050) ...... 56 Figure 2.3 Types of Baselines ...... 73
Figure 3.1 Comparison of GHG Emissions by Sector between INC and NC2 ...... 102 Figure 3.2 Trends in GDP and Electricity Consumption ...... 107 Figure 3.3 Average Malaysian Household Consumption, Based on Monthly Income ...... 115 Figure 3.4 Average Annual Energy Consumption by Household Income Level ...... 117
Figure 4.1 Top-Down and Bottom-Up Analytical Framework...... 124 Figure 4.2 Systems Boundary ...... 133 Figure 4.3 Operational Phase of Building Life-Cycle Included in the CCM ...... 138
Figure 5.1 Location of Case Study in Kuala Lumpur Boundary ...... 145 Figure 5.2 UNEP-SBCI Common Carbon Metric Data Collection Protocol ...... 147 Figure 5.3 Implementing UNEP-SBCI's CCM into Case Study ...... 149 Figure 5.4 Survey Protocol ...... 159 Figure 5.5 A Framework for Recording End-Use Patterns of Electricity Consumption ...... 161
Figure 6.1 Analytical Framework ...... 177 Figure 6.2 Research Findings Integrated into the Analytic Framework ...... 185
Figure 7.1 Daily Average Operating Time for Artificial Lighting ...... 190 Figure 7.2 Daily Average Operating Time for Artificial Cooling ...... 193 Figure 7.3 Daily Average Operating Time for Hot Water Systems ...... 195 Figure 7.4 Daily Average Operating Time for Refrigeration ...... 196 Figure 7.5 Daily Average Operating Time for Entertainment and Technology ...... 197 Figure 7.6 Daily Average Operating Time for Cooking and Kitchenware ...... 199 Figure 7.7 Daily Average Operating Time for Clothes Washing ...... 200 Figure 7.8 Analysis of End-Use Consumption Patterns of Electrical Appliance in Operating Time ...... 202 Figure 7.9 Average Daily Consumption of Electrical Appliance (n=281) ...... 204 Figure 7.10 Average Monthly Income by Percentage (%) of Households Surveyed...... 209 Figure 7.11 Average Monthly Expenditure for Rent/Housing Loan by Percentage (%) of Households...... 211 Figure 7.12 Average Monthly Expenditure for Electricity by Percentage (%) of Households ...... 212 Figure 7.13 Average Monthly Expenditure on Other Utilities by Percentage (%) of Households ...... 213 Figure 7.14 Average Percentage of Monthly Income and Expenditure Analysis (n=281) ...... 216
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Measuring Electricity-Related GHG Emissions and the Affordability of Electricity in Malaysian Low-Cost Nov 2013 Housing: A Case Study of Low-Cost Housing Projects in Kuala Lumpur
List of Tables
Table 1.1 Housing Units Built Between 2006 to 2010 (during the Ninth Malaysia Plan) ...... 10 Table 1.2 Low-Cost Housing Thresholds Defined by the Ministry of Housing and Local Government ...... 13 Table 1.3 Housing Price by Subcategory in the Kuala Lumpur Structure Plan 2010 ...... 13 Table 1.4 Building Sector’s Global Contribution to Climate Change ...... 24 Table 1.5 Average Household Energy Consumption ...... 30
Table 2.1 Summary of Federal regulations affecting low-cost housing provision in Malaysia ...... 48 Table 2.2 Energy Efficiency Strategies and Building Codes for South East Asian Countries ...... 69 Table 2.3 Brief Summary of Building Environmental Assessment (BEA) Tools ...... 80 Table 2.4 Building Life-Cycle Phase regarding Emissions Assessment ...... 81
Table 3.1 Energy Policy Development in Malaysia ...... 103 Table 3.3 Average Operating Time of Electrical Appliances ...... 111 Table 3.4 Benchmarks used in measuring affordability (% of total household income/expenditure) ...... 116 Table 3.5 Average Annual Energy Consumption by Household Income Level by TNB ...... 118 Table 3.6 Exchange Rate Table from Ringgit Malaysia to US Dollar and Australia Dollar ...... 118
Table 4.1 Sample Size Calculation ...... 131 Table 4.2 Selected PPR Low-Cost Housing Characteristics and Sample Size ...... 131 Table 4.3 Emblematic Case Study Similar Characteristics ...... 135 Table 4.4 Residential Sectors in Malaysia ...... 136 Table 4.6 Three Scope of Emission Covered in the CCM ...... 139
Table 5.1 Fieldwork Execution Plan ...... 144 Table 5.2 Analysis of Affordability ...... 162
Table 6.1 National Electricity Consumption for 2010 ...... 167 Table 6.2 Calculated Electricity Consumption for the *Building Stock ...... 167 Table 6.3 Fuel Type in Generation Mix for Electricity Produced by TNB and Calculated Fuel Consumption for *Building Stock ...... 168 Table 6.4 Results Summary of Top-Down Approach for *Building Stock ...... 169 Table 6.5 Data Collection for the Bottom-Up Approach STEP 1-3 ...... 169 Table 6.6 Electricity Consumption for Total of 12 Blocks for PPR Intan Baiduri Low-Cost Housing Project ...... 171 Table 6.7 Total Electricity Consumption for PPR Beringin ...... 172 Table 6.8 Total Electricity Consumption for PPR Intan Baiduri ...... 172 Table 6.9 Total Electricity Consumption for Both PPR Low-Cost Housing Projects ...... 173 Table 6.10 Data Derived from the Bottom-Up Approach STEP 4 ...... 174 Table 6.11 Fuel Type in Generation Mix for Electricity Produced by TNB and Calculated Fuel Consumption for Sample Size ...... 175 Table 6.12 Data Summary for Bottom-Up Approach for Sample Size of 383 Household Units ...... 176 Table 6.13 Performance Benchmarks for Sample Size against *Building Stock (or Whole) ...... 176 Table 6.14 Comparison between Case Study Findings and Other Household Energy Consumption Benchmarks .. 178 Table 6.15 Comparison of Findings with other Residential Buildings Emissions Benchmarks ...... 180 Table 6.16 Systems Boundaries for CCM’s Top-Down and Bottom-Up Approaches ...... 182 Table 6.17 Summary of Analysis from Top-Down and Bottom-Up Approaches...... 184
Table 7.1 Basic Demographic Findings ...... 189
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Measuring Electricity-Related GHG Emissions and the Affordability of Electricity in Malaysian Low-Cost Nov 2013 Housing: A Case Study of Low-Cost Housing Projects in Kuala Lumpur
Table 7.2 Average Household Daily Artificial Lighting Operating Hour (n=281) ...... 190 Table 7.3 Comparison of Average Household Lighting Consumption ...... 191 Table 7.4 Average Household Daily Artificial Cooling Operating Hour (n=281) ...... 193 Table 7.5 Average Household Daily Hot Water Systems Operating Hour (n=281) ...... 194 Table 7.6 Average Household Daily Refrigeration Operating Hour (n=281) ...... 195 Table 7.7 Average Household Daily Entertainment and Technology Operating Hour (n=281) ...... 197 Table 7.8 Average Household Daily Cooking and Kitchen Ware Operating Hour (n=281) ...... 198 Table 7.9 Average Household Daily Clothes Washing Operating Hour (n=281) ...... 200 Table 7.10 Average Daily Operating Hour of Electricity for Surveyed Appliances (%) (n=281) ...... 201 Table 7.11 Comparison of Case Study Average Operating Time with Tang (2005) and (Saidur et al., 2007) ...... 205 Table 7.12 Average Power Consumed by End-Use Electrical Appliance ...... 207 Table 7.13 Average Monthly Household Income Summary (%) n=281 ...... 208 Table 7.14 Exchange Rates for Income, Rent, Electricity and Other Utilities to USD and AUD Currency ...... 210 Table 7.15 Average Percentage of Monthly Expenditure for Rent/Housing Loan Summary (n=281) ...... 210 Table 7.16 Average Percentage of Monthly Electricity Bill Summary (n=281) ...... 211 Table 7.17 Average Percentage of Monthly Expenditure for Other Utilities Summary (n=281) ...... 213 Table 7.18 Affordability Analysis ...... 215 Table 7.19 Percentage of Household’s Operational Expenditure within 10% of Average Household Income Range (n=281) ...... 217
Table 8.1 Research Findings in Comparison to Other Research ...... 227
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Measuring Electricity-Related GHG Emissions and the Affordability of Electricity in Malaysian Low-Cost Nov 2013 Housing: A Case Study of Low-Cost Housing Projects in Kuala Lumpur
Table of Contents
Abstract ...... i Acknowledgements ...... iii Abbreviations and Units ...... iv List of Figures ...... v List of Tables ...... vi Chapter 1: Low-Cost Housing and Its Impact on Climate Change in Malaysia ...... 1 1.1 Introduction ...... 1 1.2 Lack of Environmental Research for Low-Cost Housing ...... 5 1.2.1 Lack of Environmental and Climatic Consideration in CIS 1 and 2 ...... 6 1.2.2 Public Low-cost Housing in Malaysia ...... 8 1.2.3 Long-term and Operational Affordability of Public Low-Cost Housing Projects ...... 11 1.3 Malaysian Energy Consumption and Electricity Related Emission in Buildings ...... 15 1.3.1 Lack of Environmental Research and Awareness in the Malaysian Residential Sector ... 16 1.3.2 The Need for Energy Efficiency Building Codes in Malaysia ...... 18 1.4 Global Context and Definition: Buildings & Climate Change ...... 21 1.4.1 The Building Sector’s Global Impact on Climate Change ...... 22 1.4.2 The Building Sector’s Energy and GHG Emissions Scenarios ...... 24 1.4.3 Preventing the Lock-In Effect ...... 26 1.4.4 Pattern of End-Use Electricity Consumption in Households Worldwide ...... 27 1.4.5 Energy and Emissions Reduction Strategies in the Building Sector ...... 31 1.4.6 Policy Development and Energy Efficiency in Buildings ...... 32 1.5 Research Objectives and Questions ...... 33 1.6 Research Design ...... 35 1.7 Thesis Structure ...... 37 1.8 Limitations of Research ...... 40 1.9 Implications ...... 42 Chapter 2: Buildings & Climate Change. The Need for Building Energy & Mitigation Policy in Malaysia ...... 45 2.1 Introduction ...... 45 2.2 Lack of Environmental Guidance in National Housing Policy ...... 46 2.2.1 Energy Efficiency Voluntary Measures and Incentives ...... 49 2.2.2 The Malaysian Green Building Council and Green Building Index ...... 51 2.3 Implementing Energy Efficiency and Energy Performance Building Codes ...... 53
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Measuring Electricity-Related GHG Emissions and the Affordability of Electricity in Malaysian Low-Cost Nov 2013 Housing: A Case Study of Low-Cost Housing Projects in Kuala Lumpur
2.3.1 Energy Performance Standards in the Building Sector ...... 58 2.3.2 Low and Zero Energy Performance Targets for Buildings ...... 60 2.3.3 Mandatory and Voluntary Energy Efficiency Measures ...... 63 2.3.4 Energy Efficiency Legislation and Policies in South East Asia ...... 66 2.4 The Need for Baselines to Inform Policy Development ...... 70 2.4.1 Different Types of Baselines ...... 73 2.4.2 Bottom-Up Baselines ...... 74 2.4.3 Top-Down Baselines ...... 76 2.4.4 Hybrid Baselines ...... 77 2.5 Existing Methods for Measuring the Building Sector’s Climate Impact ...... 77 2.5.1 Building Environmental Assessment (BEA) Tools ...... 78 2.5.2 The Need for a Universal Method of Measuring GHG Emissions from Building Operation 81 2.5.3 Existing GHG Accounting Tools ...... 82 2.5.4 The UNEP-SBCI’s Common Carbon Metric as Universal Tool to Measure Operational GHG Emissions ...... 87 2.5.5 Predicted and Measured Performance ...... 89 2.7 Summary ...... 92 Chapter 3: Energy & Climate Change Lock-In Risks in the Malaysian Building Sector...... 95 3.1 Introduction ...... 95 3.2 Building in the context of Environmental and Energy Policy Development in Malaysia ...... 96 3.2.1 Malaysian Environmental Policy ...... 97 3.2.2 Development of Energy Policy in Malaysia ...... 102 3.3 A Project-Specific Baseline using UNEP-SBCI’s CCM ...... 105 3.3.1 Electricity Consumption as Primary Indicator of GHG Emission ...... 105 3.3.2 Baseline End-Use Energy use of Malaysian Households ...... 108 3.4 Climate Change and Affordability ...... 111 3.4.1 Affordability, Energy Poverty and Rebound Effect ...... 112 3.4.1 Housing Affordability ...... 114 3.4.2 Affordability of Electricity for Malaysian Low-Income Households ...... 117 3.5 Summary ...... 119 Chapter 4: Research Design: Case-Study, Baselines & Systems Boundary ...... 122 4.1 Introduction ...... 122 4.2 Analytical Framework ...... 123
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Measuring Electricity-Related GHG Emissions and the Affordability of Electricity in Malaysian Low-Cost Nov 2013 Housing: A Case Study of Low-Cost Housing Projects in Kuala Lumpur
4.2.1 Piloting the Case Study ...... 125 4.3 Adopting a Survey Questionnaire ...... 126 4.3.1 Sampling Techniques ...... 127 4.3.2 Survey Types ...... 128 4.3.3 Considerations and Limitations in Conducting a Survey Questionnaire ...... 129 4.3.4 Confidence Level and Sample Size ...... 130 4.4 Systems Boundary ...... 132 4.4.1 Energy Boundary – Electricity from National Grid ...... 134 4.4.2 Location Boundary – Kepong District in Kuala Lumpur ...... 134 4.4.3 Building Typology Boundary – PPR Low-Cost Housing Projects ...... 135 4.4.4 Life-Cycle Boundary – Operational Phase ...... 137 4.4.5 Considerations and Limitations Prior to Conducting Case Study ...... 140 4.5 Summary ...... 141 Chapter 5: Case Study Protocol ...... 144 5.1 Introduction ...... 144 5.2 Adopting the UNEP-SBCI Common Carbon Metric ...... 146 5.2.1 Direct and Indirect Consumption of Purchased Electricity ...... 146 5.2.2 Data Collection for CCM Top-Down Approach ...... 150 5.2.3 Data Collection for CCM Bottom-Up Approach ...... 153 5.3 Survey Protocol for Patterns of End-Use Consumption and Affordability of Electricity ...... 157 5.3.1 Basic Demographics ...... 160 5.3.2 Investigating Average Daily Consumption of Electrical Appliances ...... 160 5.3.3 Investigating Long-Term Operational Affordability ...... 162 5.4 Summary ...... 162 Chapter 6: Baseline Electricity Consumption & Associated GHG Emissions: Results from the Common Carbon Metric ...... 165 6.1 Introduction ...... 165 6.2 Implementing the UNEP-SBCI CCM in Case Study ...... 165 6.2.1 Top-Down Approach ...... 166 6.2.2 Bottom-Up Approach ...... 169 6.3 Analysis of Findings for Both Top-Down and Bottom-Up Approaches ...... 177 6.3.1 Energy Performance Analysis ...... 178 6.3.2 Operational GHG Emission Analysis ...... 179 6.3.3 Discrepancy between Top-Down and Bottom-Up Analysis ...... 181
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Measuring Electricity-Related GHG Emissions and the Affordability of Electricity in Malaysian Low-Cost Nov 2013 Housing: A Case Study of Low-Cost Housing Projects in Kuala Lumpur
6.4 Summary ...... 184 Chapter 7: Patterns & Affordability of Electricity Consumption: Survey Results ...... 188 7.1 Introduction ...... 188 7.2 Basic Demographics ...... 188 7.3 Average End-use Operating Time of Electrical Appliances ...... 189 7.3.1 Artificial Lighting Appliances ...... 190 7.3.2 Artificial Cooling Appliances ...... 192 7.3.3 Hot Water Systems ...... 194 7.3.4 Refrigeration ...... 195 7.3.5 Entertainment and Technology Appliances...... 196 7.3.6 Cooking and Kitchen Ware Appliances ...... 198 7.3.7 Clothes Washing Appliances ...... 199 7.3.8 Summary of End-Use Electricity Consumption ...... 201 7.4 Household Income and Affordability ...... 208 7.4.1 Monthly Expenditure for Rent/Housing Loan ...... 209 7.4.2 Monthly Expenditure for Electricity ...... 211 7.4.3 Monthly Expenditure for Other Utilities ...... 212 7.4.4 Summary of Operational Affordability for PPR Low-Cost Housing Projects ...... 214 7.5 Summary and Implications ...... 217 Chapter 8: Conclusions and Recommendations ...... 221 8.1 Reprise ...... 221 8.2 End-Use Electricity Performance and Operational GHG Emissions and Informing Policy .... 226 8.2.1 Developing GHG Performance Baseline for Malaysian Building Stock ...... 226 8.2.2 End-Use Patterns of Electricity Consumption in PPR Low-Cost Housing ...... 229 8.2.3 The Need for Developing Energy Efficiency Building Codes ...... 230 8.4 Affordability of Electricity in PPR Low-Cost Housing (Case Study) ...... 232 8.5 Limitations to Research ...... 234 8.6 Implications and Contribution to Knowledge ...... 235 8.7 Further Research ...... 236 References ...... 239 Appendix 1.1 Tenaga Nasional Berhad Domestic Tariff ...... 260 Appendix 2.1 Carbon Disclosure Project Reporting Guidance Methodology (Carbon Disclosure Project, 2013b, 2013c) ...... 261 Appendix 2.2 UNEP-SBCI’s Common Carbon Metric Building Definition (UNEP-SBCI, 2010b) ...... 263
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Measuring Electricity-Related GHG Emissions and the Affordability of Electricity in Malaysian Low-Cost Nov 2013 Housing: A Case Study of Low-Cost Housing Projects in Kuala Lumpur
Appendix 3.1 Construction Industry Master Plan (CIMP) ...... 264 Appendix 3.2 Types of Public Low-Cost Housing in Malaysia...... 266 Appendix 3.3 Final Electricity Consumption ...... 268 Appendix 3.4 Average Operating Hour of Electrical Appliances ...... 269 Appendix 4.1 MTEN PPR Housing List (City Hall of Kuala Lumpur, 2009c) ...... 272 Appendix 4.2 Sampling Size Calculation (QI Macros, 20120) ...... 273 Appendix 4.3 Execution Plan for Field Work ...... 274 Appendix 5.1a UNEP-SBCI Common Carbon Metric Top-Down Approach – Building Stock Characteristics STEPs 1-3 ...... 275 Appendix 5.1b UNEP-SBCI Common Carbon Metric Top-Down Approach – Electricity Consumption of the Building Stock STEP 4 ...... 276 Appendix 5.1c UNEP-SBCI Common Carbon Metric Top-Down Approach – Fuel Consumption STEP 5 277 Appendix 5.1d UNEP-SBCI Common Carbon Metric Top-Down Approach – Fuel Consumption STEP 6 278 Appendix 5.1e UNEP-SBCI Common Carbon Metric Top-Down Approach – Combined Emissions and Energy Consumption ...... 279 Appendix 5.1f UNEP-SBCI Common Carbon Metric Top-Down Approach – Performance Metrics 280 Appendix 5.1g UNEP-SBCI Common Carbon Metric Top-Down Approach – Performance Graph.. 281 Appendix 5.2a UNEP-SBCI Common Carbon Metric Bottom-Up Approach –Data Characteristics . 282 Appendix 5.2b UNEP-SBCI Common Carbon Metric Bottom-Up Approach – Building Characteristics (STEPs 1-3) 283 Appendix 5.2c UNEP-SBCI Common Carbon Metric Bottom-Up Approach – Electricity Consumption of Individual Households (STEP 4) ...... 284 Appendix 5.2d UNEP-SBCI Common Carbon Metric Bottom-Up Approach – Electricity Consumption for the PPR Low-Cost Housing Project (STEP 4) ...... 285 Appendix 5.2e UNEP-SBCI Common Carbon Metric Bottom-Up Approach – Electricity Consumption (STEP 5) 286 Appendix 5.2f UNEP-SBCI Common Carbon Metric Bottom-Up Approach – Emission and Electricity Consumption Summary ...... 287 Appendix 5.4a Questionnaire Sample – Basic Demographics ...... 288 Appendix 5.4b Questionnaire Sample – Electricity Consumption ...... 289 Appendix 5.4c Questionnaire Sample – Summary of Electricity Consumption for both PPR Low-Cost Housing Projects 291 Appendix 5.4d Questionnaire Sample – Household Income and Expenditure ...... 292 Appendix 5.4e Questionnaire Sample – Electricity and Other Utilities Expenditure ...... 293 Appendix 6.1a STEPS 1-3: CCM Top-Down Approach (UNEP-SBCI, 2011) ...... 294
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Measuring Electricity-Related GHG Emissions and the Affordability of Electricity in Malaysian Low-Cost Nov 2013 Housing: A Case Study of Low-Cost Housing Projects in Kuala Lumpur
Appendix 6.1b STEP 2: Total Existing Residential Property for 2010 (NAPIC, 2012) ...... 295 Appendix 6.1c STEP 4a: CCM Top-Down Approach (UNEP-SBCI, 2011) ...... 296 Appendix 6.1d STEP 4b: CCM Top-Down Approach (UNEP-SBCI, 2011) ...... 297 Appendix 6.1e STEP 5a: CCM Top-Down Approach (UNEP-SBCI, 2011) ...... 298 Appendix 6.1f STEP 5b: CCM Top-Down Approach (UNEP-SBCI, 2011) ...... 299 Appendix 6.1g CCM Top-Down Approach Performance Metrics Summary (UNEP-SBCI, 2011) ..... 300 Appendix 6.1h CCM Top-Down Approach Performance Metrics Graph (UNEP-SBCI, 2011) ...... 301 Appendix 6.2a STEP 1-3: CCM Bottom-Up Approach Performance (UNEP-SBCI, 2011) ...... 302 Appendix 6.2b STEP 4: CCM Bottom-Up Approach Performance (UNEP-SBCI, 2011) ...... 303 Appendix 6.2c STEP 5a: CCM Bottom-Up Approach Performance (UNEP-SBCI, 2011) ...... 304 Appendix 6.2d STEP 5b: CCM Bottom-Up Approach Performance (UNEP-SBCI, 2011) ...... 305 Appendix 6.2e STEP 5c: CCM Bottom-Up Approach Performance (UNEP-SBCI, 2011) ...... 306 Appendix 7.1a Average Consumption of Electrical Appliances for PPR Beringin ...... 307 Appendix 7.1b Average Consumption of Electrical Appliances for PPR Intan Baiduri ...... 308 Appendix 7.2a Average Monthly Household Income for PPR Beringin ...... 309 Appendix 7.2b Average Monthly Household Income for PPR Intan Baiduri ...... 310 Appendix 7.3a Average Monthly Rent/Housing Loan Expenditure for PPR Beringin ...... 311 Appendix 7.3b Average Monthly Rent/Housing Loan Expenditure for PPR Intan Baiduri ...... 312 Appendix 7.4a Average Monthly Electricity Bill Expenditure for PPR Beringin ...... 313 Appendix 7.4b Average Monthly Electricity Bill Expenditure for PPR Intan Baiduri ...... 314 Appendix 7.5a Average Monthly Utility Expenditure for PPR Beringin ...... 315 Appendix 7.5b Average Monthly Utility Expenditure for PPR Intan Baiduri ...... 316
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Measuring Electricity-Related GHG Emissions and the Affordability of Electricity in Malaysian Low-Cost Nov 2013 Housing: A Case Study of Low-Cost Housing Projects in Kuala Lumpur
Table of Contents
Measuring Electricity-Related GHG Emissions and the Affordability of Electricity in Malaysian Low-Cost Nov 2013 Housing: A Case Study of Low-Cost Housing Projects in Kuala Lumpur
Chapter 1: Low-Cost Housing and Its Impact on Climate Change in Malaysia
1.1 Introduction
The main aim of this research is to present measurable, reportable and verifiable (MRV) data to support policy development for energy efficient and affordable urban residential buildings in Malaysia. There are three main objectives to this research, the first and most significantly, is producing measurable, reportable and verifiable data of electricity consumption and greenhouse gas (GHG) emissions in public low-cost housing in order to develop a baseline. The second objective of this research is to understand the end-use pattern of electricity consumption in public low-cost housing, to further inform policy development by analysing both objectives’ findings. The third and final objective is to investigate long-term and operational affordability of these public low-cost housing projects, by surveying the average household income and average monthly expenditure for rent, electricity and other utilities.
The outcomes of this research are therefore intended to inform policy makers of the contribution more energy efficient affordable housing can play in meeting Malaysia’s voluntarily commitment to reduce 40% of its greenhouse gas (GHG) emissions (from 1990 levels) by year 2020, announced at the 2009 United Nations Climate Change Conference in Copenhagen (COP-15) (Department of Environment, 2010). However, this commitment has not been greeted with much optimism given limited support from existing legislation and restrained environmental awareness (Department of Environment, 2010). Currently, there is no legislation that holds environmental sustainability mandatory for major GHG emitting sectors such as energy, transportation, and oil and gas (Department of Environment, 2010). Malaysia has rapidly transformed from an agricultural to an industrialized economy in the last four decades, with an alarming growth of GHG emissions that are caused by the escalating number of automobiles, factories and power plants.
Chapter 1 sets the context for the research, beginning with brief descriptions of climate change and the environmental impact of the building sector. It is necessary to set the scene, beginning with the Malaysian context in order to recognize the significance and contribution of this research to the larger body of knowledge. This subsequently contributes to the research rationale and research questions.
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Malaysia’s economy and quality of life is improving, but at the cost of the environment. Malaysia is ranked 66th in the 2009 Human Development Index (HDI)1 with an annual growth of approximately 0.81% (United National Development Programme (UNDP), 2009a). Conversely, Malaysia also ranked number 54th on the Environmental Performance Index (EPI)2 in 2010, dropping from number 27th in 2008 (Yale Center for Environmental law and Policy & Center for International Earth Science Information, 2010).
Research using a long-range energy alternative planning system (LEAP) projected that without any mitigation measures, Malaysia’s carbon dioxide (CO2) emission in 2020 will amount to 285.73 million tonnes; a 68.86% increase compared to year 2000 (Safaai et al., 2010). In 2010, the International Energy Agency (IEA) reported Malaysia’s carbon emission was a total of 185 million tonnes3, which is approximately 0.6% of the global total (30,276 million tonnes of CO2) (IEA, 2012). The emissions contribution is significant when Malaysia only accounts for approximately 0.4% of the world’s population, averaging 7.5 tonnes per person (UNDP Communications Office, 2007).
Figure 1.1 illustrates Malaysian position in comparison with a number of emerging and developed nations, in terms of carbon emissions percentage change between 1990 to 2005 (World Wide Fund for Nature (WWF), 2009). Between 1990 to 2004, Malaysia’s carbon emissions grew by 221 per cent (+221%) increased energy demand from industrial and transportation sectors, dubbed the fastest growth rate in the world (Al- Jazeera, 2007; UNDP Communications Office, 2007; Watkins, 2007). By 2009, Malaysia’s national energy demand had increased by 210.7% from 1990, which prompted its carbon emissions growth by +235.6% (Energy Commission, 2011b; IEA, 2011a).
1The HDI provides a measure of human progress through the relationship between income and well-being on measures of life expectancy, literacy, enrolment in education, purchasing power parity (PPP) and income (UNDP, 2009a). 2The EPI ranks countries based on 25 performance indicators across policy categories covering environmental health and ecosystem vitality (Yale Centre for Environmental Law and Policy & Centre for International Earth Science Information Network, 2010). 3 The 2012 IEA methodology adopted an “electricity-only factor expressed in grammes of CO2 per kWh” to replace its former indicator of “CO2 emissions per kWh for the electricity and heat generating industries” (IEA, 2012).
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Figure 1.1 Percentage Change in Carbon Emissions from Fossil Fuel Use (1990-2005) Source: WWF (2009)
Figure 1.2 presents a more contemporary comparison of Malaysia’s steady increase of carbon emissions (metric tons per capita) to the world average emissions and other developing countries in Asia such as China, India and Indonesia, and comparing Malaysia’s emission with its neighbouring Singapore where there has been a steady decrease in emissions (World Bank, 2013). The unparalleled carbon emission growth, coupled with business-as-usual practices will potentially lock Malaysia in for an unsustainable path of development. Malaysia clearly has to make significant and urgent changes in its policy, economy, industries and lifestyle if it is to reduce its contribution to climate change. Without emissions mitigation and conservation policies, Malaysia is unlikely to meet its emissions reduction targets.
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Figure 1.2 Carbon Emissions in Metric Tons per Capita (2003-2010) Source: World Bank (2013)
Notwithstanding its environmental impacts, the building sector has been identified by the Intergovernmental Panel on Climate Change (IPCC) as the sector with largest mitigation potential (IPCC, 2007a). It is estimated that both new and existing buildings have the potential to reduce energy consumption up to 80% using proven and commercially available technologies and with net profit during their lifespan (IPCC, 2007a; UNEP, 2009). Enforcing energy performance requirements in building codes has been argued to be the most cost-effective strategy in reducing GHG emissions from both existing and new buildings (UNEP, 2009).
Baseline research is necessary to inform policy development in terms of energy modelling and GHG mitigation strategies (Strachan, 2011). Baseline studies focusing on building energy performance and GHG emissions are used to monitor and measure the effectiveness of building energy performance policies (Koeppel & Urge-Vorstaz, 2007). The lack of energy performance and GHG emissions baselines limits the information policy makers have to develop emissions mitigation policies. The importance of baseline study is further discussed in Chapter 2.
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1.2 Lack of Environmental Research for Low-Cost Housing
The subject of sustainable low-cost housing has not been the focus of policy makers in Malaysia. Past research into low-cost housing mainly focused on the satisfaction level of its inhabitants, in terms of dwelling unit features, services and utilities, social environment, neighbourhood environment and public facilities (see Khair et al., 2012; Mohit et al., 2010; Omar, 2008; Salleh, 2008; Sulaiman & Yahaya, 1987). There is a clear gap in environmental research for this specific residential typology, and significantly as all housing developers in Malaysia are obliged to allocate 30% of new residential construction to low-cost housing within its development project unless expressly permitted otherwise (Aziz, 2007; EPU, 2006; Real Estate and Housing Developers' Association (REHDA), 2008).
The government’s response to the increasing housing demand in urban areas for the lower-income population is manifested through low-cost housing projects, with affordable sale price and/or low monthly rental. Most low-cost housing projects in urban areas are done on a large scale and are constructed according to Construction Industry Standards for Low-Cost Housing (CIS 1 and CIS 2) (Ismail, 2003; Shaari, 2003; Sufian, 2007). This research intends to fill this gap and better enable Malaysia to develop strategies for meeting its emissions reduction targets and monitoring its progress.
Therefore this research presents measurable, reportable and verifiable (MRV) data of electricity consumption and GHG emissions of low-cost housing in terms of kWh/m2/year and kWh/occupant/year, and kgCO2e./m2/year and kgCO2e./occupant/year. Such data was collected from the energy provider company, Tenaga Nasional Berhad – TNB, and using the United Nations Environment Programme - Sustainable Buildings and Climate Initiative’s (UNEP-SBCI) Common Carbon Metric (CCM). The CCM for buildings is a tool to measure, report and verify baseline levels of GHG emissions associated with operational building energy use, in a consistent and comparable way (UNEP-SBCI, 2010b). The data utilized electricity bills are measured and quantifiable data from TNB, and is reported using the CCM. The findings can be verified using simple power meter to calculate household average monthly electricity consumption. However this lies outside of the research scope, and can be suggested for further research.
The need to conduct a baseline study on the performance of the existing building stock is a matter of urgency because of the sheer quantity of floor space in existence and being
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planned. Furthermore, developing an understanding of the energy and GHG emissions baselines of the public low-cost housing typology, which is standardized through CIS 1 and CIS 2, has the potential to bring nation-wide change. A more detailed description of public low-cost housing and CIS is presented in Chapter 3. Low-cost housing in Malaysia represented 23.3% of housing provision targets for 2010 (EPU, 2006). However, in terms of a life-cycle costing, affordability of low-cost housing in Malaysia excludes long-term operational cost.
1.2.1 Lack of Environmental and Climatic Consideration in CIS 1 and 2
Additional to the UBBL, low-cost housing projects are also subject to specific guidelines for construction i.e. the Construction Industry Standard (CIS) 1 and 2 (Ismail, 2003; Shaari, 2003; Sufian, 2007). There is a complete lack of environmental and climatic consideration in the current Construction Industry Standard (CIS) for low-cost housing. The CIS 1 is the minimum standard for construction of one to two storey low-cost housing units, while the CIS 2 is specific to low-cost housing projects that are more than two storeys high (CIDB, 1998).
Both CIS 1 and 2 are produced by the Construction Industry Development Board (CIDB), which specifies uniform design and minimum planning requirements for low- cost houses in Malaysia to ensure “housing estates for low-income dwellers are developed to minimum standards suitable for human habitation” (Ismail, 2003 p.2). The CIS 2 is divided into two scopes, i.e. site planning and individual unit specifications. Within the site planning scope, there are 12 listed specifications for (CIDB, 1998): 1) Earthworks (or ground works); 2) Floor area for residential units; 3) Lot size for residential units; 4) Distance between buildings; 5) Road network systems; 6) Water reticulation systems (or water network systems); 7) Sewerage systems; 8) Requirements for fire prevention; 9) Vehicle parking lots (for cars and motorbikes); 10) Social and community facilities; 11) Access to electricity; and 12) Landscaping
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The two standards used for low-cost residential construction cover minimum requirements on layout, space and configuration for four aspects of habitation – safety, adequate infrastructure, physical and mental health, and community (Ismail, 2003). Both CIS 1 & 2 basic requirements for individual low-cost housing units are as follows (CIDB, 1998): a) Minimum of three (3) bedrooms; b) Minimum net floor area of 60m2 for 1-2 storey units, and 63m2 for high-rise units (which is measured from centre line of wall) ; c) Separate or combined living and dining area; d) Kitchen with minimum area of 5.4m2; e) Separate bathroom (minimum area of 1.8m2) and a toilet (minimum area of 1.8m2); and f) Storage and clothes drying area.
However, based on the definition provided by the City Hall of Kuala Lumpur’s Housing Management Department, the PPR low-cost housing units are built with a minimum of 650 square feet (ft2), (60m2) (City Hall of Kuala Lumpur, 2009a). CIS 1 and 2 are used as the national minimum construction standard for both public and private low-cost housing projects in Malaysia (CIDB, 1998). Consequently, as this research intends to inform policy on a national scale, the minimum net floor area of a PPR low-cost housing unit is defined as 63m2 for high-rise units, following the CIS 2.
There is a slow growing awareness for the need to incorporate more environmentally sustainable design in the Malaysian the building sector. It was identified in the Tenth Malaysia Plan as one of the three key challenges faced by the low-cost housing typology (EPU, 2010). The remaining two challenges are to match the supply with the demand for affordable housing and to increase the quality of new and existing affordable houses (EPU, 2010). Moreover, there are no specific mentions of energy efficiency, and climatic responsive design in either CIS 1 & 2. The CIS 2 also did not specify building orientation, insulation and/or shading needs. These elements are necessary in efforts to reduce cooling needs and heat transfer in tropical climate buildings (IEA, 2008; Levine et al., 2007).
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Furthermore, past research into low-cost housing mainly focused on the satisfaction level of its inhabitants, in terms of dwelling unit features, social environment, neighbourhood and public facilities (see Mohit et al., 2010; Omar, 2008; Salleh, 2008; Sulaiman & Yahaya, 1987). Tapsir (2005) suggests that in order to promote sustainability, current housing policies should incorporate service life planning. Other researchers have examined indoor thermal environment of public low-cost housing through natural ventilation, and concluded that the indoor environment was uncomfortable even though in compliance with the Uniform Building By-Laws (Kin, 1998). This suggests that the current UBBL standard is inadequate and has failed to provide indoor comfort for its inhabitants. This implies that the UBBL and other construction standards need to be updated and revised to incorporate more environmental considerations, especially climate-based ones to provide indoor environmental comfort.
1.2.2 Public Low-cost Housing in Malaysia
Housing in Malaysia is divided into five (5) subcategories of housing: housing for the poor and low-cost; low-medium cost, medium cost, and high-cost housing (EPU, 2006). Low-cost housing is provided by both private (through private developers and cooperative societies) and public sectors (through housing land schemes and governmental agencies) (EPU, 2006). Public low-cost housing is provided by different agencies and authorities from Federal, State and local levels, such as the Ministry of Rural and Regional Development, National Housing Department (NHD), City and Hall of Kuala Lumpur (CHKL) (EPU, 2010; National Housing Department, 2011).
The Malaysian housing industry falls under jurisdiction of various regulations such as Housing Development (Control and Licensing) Act 1976; Street, Drainage and Building Act 1976; Town and Country Planning Act 1974; and the Uniform Buildings By-Laws 1984 (Aziz, 2007; Sufian, 2007). Any housing development in Malaysia has to abide by these various statutes before submitting application to the respective local government for approval. Theoretically, this legislation is in place to ensure quality and cohesion of development practice but enforcement of the legislation falls under the responsibility of local authorities (Aziz, 2007). The effectiveness of enforcement is then dependent on the capacity of each local authority, in terms of skills and manpower, and its approval committees (Aziz, 2007).
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The National Housing Department is the implementing agency under the Ministry of Housing and Local Government and is responsible to plan and implement affordable, comfortable and safe public housing to meet the needs of lower-income population (National Housing Department, 2009). State governments also provide public low-cost housing through private developers, where the cost of supplying public low-cost housing is subsidized from the sale of high and medium-cost housing (National Housing Department, 2011). State governments have differing quotas set to produce public low- cost housing by the private sector (National Housing Department, 2011), usually about 30% of the total housing development (EPU, 2006).
Public low-cost housing units are often subsidized by the government, between 30 to 70% of the total construction cost (EPU, 2010). According to the Ninth Malaysia Plan, public low-cost housing represented approximately 192,000 units (31%) of Malaysian’s annual housing target between 2001-2005 (EPU, 2006). However the actual low-cost housing units built by the public sector between 2006-2010 dropped to only 85,000 units per year, representing 27.9% total housing) (EPU, 2006).
Low-cost housing in Malaysia is also seen as a mandatory section of housing development, as housing developers must provide 30% of their total housing development for low-cost housing (Aziz, 2007; EPU, 2006; REHDA, 2008). Administrative procedures force developers to set aside a portion of the development project to provide low-cost housing in order to gain development approval by local authorities. However, the process has had the unintended consequence of leading to the questionable quality of these low-cost housing completions (Aziz, 2007; REHDA, 2008).
According to the Tenth Malaysia Plan (2011-2015), a total of 95,800 low-cost housing units were built during the Ninth Malaysia Plan (2006-2010) (EPU, 2010). Approximately 42,300 units of low-cost housing (44.2%) were built by the public sector4 and 53,500 units (55.8%) built by the private sector (refer Table 1.1) (EPU, 2010). However, based on a nation-wide States government census, as of 30th June 2010, there were 90,282 squatter households that had not been relocated (National Housing Department, 2011). This exemplifies insufficient provisions by both the public and private sector to meet the growing demand for low-cost housing, and the importance of
4 Excluding States and local authorities-provided housing (EPU, 2010), therefore implies the low-cost housing units built are Federal government funded low-cost housing.
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low-cost housing within the residential context. This growing demand is due to a growing urban population, where the housing market is extensive and purchasing power is higher than rural areas (National Housing Department, 2011). Low quality of construction for ‘affordable’ or low-cost housing were also identified as a key issue in the Tenth Malaysia Plan and the National Housing Policy (EPU, 2006; National Housing Department, 2011)
Number Houses Built, 2006 - 2010 Percentage of Total Type of Housing (Thousand units) Publicly Funded (Thousand units) Public Private Housing (%)
Housing for the Poor 31.7 0 31.7 100
Low-Cost 42.3 53.5 95.8 44
Low-Medium Cost 9.6 35 44.6 38
Medium Cost 27.2 91.0 118.2 23
High Cost 0 278.7 278.7 0
Table 1.1 Housing Units Built Between 2006 to 2010 (during the Ninth Malaysia Plan) Source: EPU (2010)
Prior to February 2002, public low-cost housing was divided into two (2) programmes, the Low-cost Housing Public Programme (PAKR), and the Integrated People’s Housing Programme for Rental – Program Perumahan Rakyat (PPR Integrated) (National Housing Department, 2008). The PAKR programme sold low-cost housing units to households earning less than RM 1,500 monthly, for RM 25,000 per unit (National Housing Department, 2008). The selling price for PAKR units was identical, regardless of State, area or location. The PPR Integrated programme rented low-cost housing units for households earning less than RM1,500 monthly, for RM124.00 per month (National Housing Department, 2008).
In February 2002, the Malaysian cabinet made changes in policy regarding the implementation of low-cost housing programmes, changing the PAKR programme that was formerly a State financed project, to Federal ownership and naming it the People’s Housing Programme (PPR Owned). According to the National Housing Department, the PPR Owned programme is currently only implemented in Pahang state, with a selling price of RM35,000 per unit (National Housing Department, 2008). The PPR Integrated
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programme remained as it was, but would now be known as PPR Rent (National Housing Department, 2008) (refer Appendix 3.2).
In Kuala Lumpur, PPR rent is further divided into two (2) categories, the National Economic Action Council’s (NEAC) PPR, and the City Hall of Kuala Lumpur (CHKL) PPR housing (City Hall of Kuala Lumpur, 2009a; National Housing Department, 2008). The NEAC PPR housing was implemented in the Seventh Malaysia Plan (1996-2000), which provided low-cost housing to relocate squatter settlements that were affected by government development projects in Kuala Lumpur and the larger Klang Valley region (National Housing Department, 2008). The Housing Management Department of City Hall of Kuala Lumpur manages both the NEAC PPR and CHKL PPR housing projects (City Hall of Kuala Lumpur, 2009b).
However in July 2009 the Prime Minister of Malaysia, Datuk Seri Najib, announced that the PPR Rent projects could be bought, an effort to help low-income households own their current rented homes (City Hall of Kuala Lumpur, 2009c). The NEAC PPR units were to be sold at RM 35,000 per unit, and the CHKL PPR at RM 21,500 to RM 35,000 per unit depending on the size and number of rooms (City Hall of Kuala Lumpur, 2009c). This sale affected 44,146 units of NEAC PPR, and 14,584 units of CHKL PPR (City Hall of Kuala Lumpur, 2009c). Tenants that rented their home for more than 10 years were eligible to buy over their PPR units, to help ensure only original tenants were eligible to purchase the unit (City Hall of Kuala Lumpur, 2009c). Hereafter, in the context of this research, PPR housing means both for rent or purchase purposes.
1.2.3 Long-term and Operational Affordability of Public Low-Cost Housing Projects
Apart from measuring GHG emissions from energy-related building operations, this research also provides an indicative report on the affordability of electricity for low- income households. According to the Central Bank, Malaysian households spend approximately 20% of household income on a combination of “housing, water, electricity, gas and fuels” (Central Bank of Malaysia, 2010 p.20).
Therefore, the percentage of monthly household income spent on electricity, which accounts for approximately 90% of the energy consumed in office buildings is important to investigate (Zain-Ahmed, 2008b). This will help measure operational and long term affordability of government-funded low-cost housing projects and help determine
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whether the affordability of electricity, along with the level of energy-related GHG emissions demonstrate a need for the development of building energy performance policies.
The specific case studies in this research are focussed on National Economic Action Council (NEAC) People’s Housing Programme – Program Perumahan Rakyat (PPR)5 low-cost housing projects, as this is the national standard of public low-cost housing projects. The demand for such housing is increasing, especially in the urban areas, and reflected in the new 2013 Federal Government’s budget, where it allocated approximately RM 5436 million to build 45 more urban PPR housing project across the country (Mustafa, 2012).
This boom in new construction presents a ‘carbon lock-in’ risk, as the residential sector is under-regulated in terms of energy efficiency or energy conservation and needs research to support the development of relevant policies to avoid the lock-in effect. A ‘carbon lock-in’ condition is manifested through “a combination of systematic forces that perpetuate fossil fuel-based infrastructure in spite of their known environmental externalities and the apparent existence of cost-neutral, or even cost-effective, remedies” (Unruh, 2000 p.817).
Low-cost housing in Malaysia can be defined by its sale price, or by its monthly rental level. Nationally, public low-cost housing units are rented out for RM 124.00 per month (City Hall of Kuala Lumpur, 2009a; National Housing Department, 2008). Meanwhile, the maximum sale price for low-cost housing is RM 42,000 per unit (City Hall of Kuala Lumpur, 2009a; National Housing Department, 2008). This is based on the price range given by the Ministry of Housing and Local Government (MHLG) (refer Table 1.2) and City Hall of Kuala Lumpur (refer Table 1.3) as two prominent low-cost housing providers.
5 Henceforth, the research adopts the acronym PPR to mean the National Economic Action Council (NEAC) People’s Housing Programme – Program Perumahan Rakyat (PPR) 6 RM 543 million is approximately USD 175 million or AUD 171 million, as exchange rate of 12th February 2013 (XE, 2013).
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HOUSEHOLD INCOME LOCATION PRICE/UNIT (RM) OF TARGET GROUPS TYPE OF HOUSING (PRICE OF LAND/SQ FT) (RM) per month (RM) A Apartment 42,000 Main cities and urban areas 1,200 to 1,500 (more than 5 storey) (RM45 and above) B Apartment 35,000 Big towns and suburban areas 1,000 to 1,350 (1-5 storeys) (RM15 to RM44) C 30,000 Suburban areas and small towns 850 to 1,200 Terrace and cluster7 (RM10-RM14) D 25,000 Rural areas 750 to 1,000 Terrace and cluster (Below RM10)
Table 1.2 Low-Cost Housing Thresholds Defined by the Ministry of Housing and Local Government Source: Ministry of Housing and Local Government (2002)
HOUSEHOLD INCOME MINIMUM FLOOR SPACE PRICE/UNIT TYPES OF HOUSING OF TARGET GROUPS (m2) (RM) per month (RM) Low-Cost 60 25,000 - 42,000 1,500 – 4,000 Low-Medium-Cost Not described 42,001 – 85,000
Medium-Cost Not described 85,001 – 150,000
Table 1.3 Housing Price by Subcategory in the Kuala Lumpur Structure Plan 2010 Source: City Hall of Kuala Lumpur (2000)
Low-cost housing in Kuala Lumpur is targeted at households earning less than RM 4,000 per month, while other urban areas in Malaysia is targeted at household earning less than RM1,500 per month (City Hall of Kuala Lumpur, 2009a; National Housing Department, 2008). Interpreting the two tables, urban low-cost housing can be defined as housing that is over five store high, for a maximum sale price of RM 42,0008, or rented out at RM 124.00 per month, for households earning less RM 1,500 a month (City Hall of Kuala Lumpur, 2009a; National Housing Department, 2008).
Therefore, low-cost housing in Kuala Lumpur can be defined as housing that is over five store high, for a maximum sale price of RM 42,000, or rented out at RM 124.00 per
7 Cluster housing can be defined as a cluster of four (4) single-storey housing units adjoined to one another by a common 8-feet wide breezeway, in a housing estate that consists of several clusters (Mohit & Nazyddah, 2009; Nooi, 1980). 8 RM 42,000 is approximately equivalent to AUD 12,996 or USD 13,525; and RM 124.00 is approximately equivalent to AUD 38.37 or USD 39.93. Exchange rate as of 3rd February 2013 (XE, 2013).
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month, for households earning less RM 4,0009 a month (City Hall of Kuala Lumpur, 2009a; National Housing Department, 2008). Hereafter, this definition of low-cost housing in Kuala Lumpur is adopted for this research, to avoid confusion between low- cost and affordable housing. Consequently, operational costs of these low-cost housing projects should be investigated to ensure long-term affordability for low-income households.
The current definition of low-cost housing in Malaysia excludes operational costs, therefore operational and long-term affordability remain uncertain. Consequently, operational affordability of pubic low-cost housing, in terms of apportionment/percentage of average household income spent on operational household expenditure such as rent, electricity and other utilities, is also investigated in this research by using a survey questionnaire and interview techniques.
Affordability in housing is often defined by the ratio of expenditures, such as rent and housing loan repayments, to total household income (Fankhauser & Tepic, 2005, 2007; Mulliner et al., 2013). This housing affordability definition often overlooks other important issues such as environmental and social sustainability of housing (Mulliner et al., 2013), where a typical low-income household spends a substantial share of monthly income on energy and utility services such as electricity, heating and water (Fankhauser & Tepic, 2005, 2007).
In addition, due to Malaysia’s electricity price being relatively cheap with high subsidies by the government (Mongia et al., 2007), there is risk of direct rebound. Direct rebound effect can be defined as increased consumption of energy services induced by price reduction, through increased production efficiency (Herring, 2006; Maxwell et al., 2011; WBCSD, 2008). While investigating the risks of direct rebound effect is important, this lies beyond the scope of this research. However and indirectly, MRV data of household income and expenditure on electricity represented in this research can contribute to informing such risks. Therefore, investigating the percentage of monthly household income spent on electricity will contribute to the understanding of long-term energy affordability and effectiveness of subsidies for low-income households. Discussion on climate change and affordability of housing is also presented in Chapter 3.
9 RM 4,000 is approximately equivalent to AUD 1,237 or USD 1,288. Exchange rate as of 3rd February 2013 (XE, 2013).
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Therefore the research rationales are reiterated as:
1) No energy-related GHG emission baseline data of building operation for the chosen building typology; 2) No energy efficiency legislation for the building sector so there is a risk of energy inefficiency; 3) Lack of environmental research in the residential sector, particularly for public low-cost housing; 4) Affordability in low-cost housing excludes operational costs so long-term affordability is uncertain.
In summary, key issues addressed in this research are end-use electricity consumption and GHG emissions, baseline data, lock-in effect and affordability. These issues are further explored in Chapters 2 and 3. Against the Malaysian context, a more global picture is presented next, to highlight the significance of the key issues addressed in this research.
1.3 Malaysian Energy Consumption and Electricity Related Emission in Buildings
Malaysia’s final energy demand in 2000 was predominantly used for transportation (40.6%10) and industrial purposes (38.4%), and is expected to grow at an annual rate of 4.8% from 2000 to 2020 with a business-as-usual (BAU) scenario (Energy Commission, 2011b; Ministry of Natural Resources and Environment (MNRE), 2011). Malaysia’s commercial and residential building sectors accounted for approximately 13% of national energy demand (Energy Commission, 2011b; Economic Planning Unit (EPU), 2006). Additionally, the commercial and residential buildings consumed 48% of final electricity demand for the same year (Energy Commission, 2011b; EPU, 2006).
Electricity is the focal point of investigation in this research because electricity is the main energy source consumed in Malaysian buildings, and its consumption growth is higher in buildings than in other sectors (UNDP, 2011). The single largest electricity consumer in Malaysia (for 2009) is the domestic sector (82.8%), followed by commercial
10 Transportation = 12,071 ktoe*; Industrial = 11,406 ktoe; Residential and commercial = 3,868 ktoe; National energy demand = 29,699 ktoe (Energy Commission, 2011b; Ministry of Natural Resources and Enviroment, 2011). *ktoe = kilo tonne of oil equivalent (IEA, 2011g).
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(16.1%), public lighting (0.7%), industrial (0.4%), agriculture (0.1%), and mining (0.01%) (Energy Commission, 2009). Electricity is also the single largest GHG emitting sector in Malaysia, represented approximately at 43% of total emissions annually (EPU, 2006; Safaai et al., 2010; Zain-Ahmed, 2008b).
Between 2000 to 201011, total energy demand and final electricity consumption by the commercial and residential sector increased by approximately 80% and 51% respectively (Energy Commission, 2011b). In 2007, GHG emissions from Malaysian buildings accounted for approximately 4% of national emissions related to energy, at 3,947
Gigagram of carbon dioxide (GgCO2) or approximately 0.004 Giga-tonnes of carbon dioxide (GtCO2) (Malaysia Energy Centre, 2007). The average energy consumption and GHG emissions for the Malaysian building sector12 is expected to grow approximately at 6% rate annually (UNDP, 2011).
Zain-Ahmed (2008b) estimated that the average Malaysian office building consumes energy at approximately 269 kilowatt per meter square per year (kWh/m2/year). However, energy consumption data for disaggregated building typologies are unavailable, as the national database combines both residential and commercial buildings in one category. This is exemplified according to the data presented above and in the National Energy Balance database supplied by the Malaysian Energy Commission (Energy Commission, 2011b). Identifying how energy is consumed within each typology of the built environment helps identify strategies for mitigation and energy consumption reduction (UNEP, 2009). The development of energy performance policies in Malaysia therefore requires research that provides further disaggregation of energy use by building type. This research has set out to address this knowledge gap.
1.3.1 Lack of Environmental Research and Awareness in the Malaysian Residential Sector
The building sector presents a huge potential for GHG mitigation strategies, but has been poorly understood in Malaysia. Although awareness towards sustainability is increasing in the Malaysian building industry, there is still a lack of implementation of strategies and a need for mandatory policies (Newell & Manaf, 2008; Zainul Abidin,
11 Energy demand commercial and residential sector in 2010 = 6,951 ktoe (Energy Commission, 2011b). 12 “The building sector in Malaysia consist predominantly of commercial, government and residential buildings (high-rise, as well as terraced and single dwellings). Industrial facilities obviously also have buildings, but energy use in industry is dominated by processing and building energy use is therefore a minor constituent” (UNDP, 2011 p.5).
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2010; Zainul Abidin, 2009). Research regarding GHG emissions from the Malaysian building sector has only considered urban scale emissions (Fong et al., 2008; Wee et al., 2007), cement manufacturing (Fujita et al., 2009), and embodied emissions of building materials for residential homes (Mursib, 1999; Zakaria, 2007).
Research and development initiatives invested in energy efficient buildings remain a low priority to both industry and building owners (UNDP, 2011). Research regarding energy efficiency or environmental performance of buildings in Malaysia focuses on technological issues for high performance “green buildings” (Shafii, 2007; Zain-Ahmed, 2008b), and not GHG emissions from end-use energy consumption. Such research and development outputs can be seen in some government or institutional buildings and private commercial properties. For example, the Zero Emission Office (ZEO) and the Low Energy Office (LEO) were construction as governmental headquarters for the Malaysian Energy Centre and Ministry of Energy, Green Technology and Water (MEGTW), respectively (Shafii, 2007; Zain-Ahmed, 2008b). Other institutional buildings include the Security Commission Headquarters, the Telekom Headquarters and the Energy Commission ‘diamond building’ (Shafii, 2007; Zain-Ahmed, 2008b).
Still, these buildings were built as case studies to demonstrate, educate and enhance energy efficiency awareness to the building industry and the public (Zain-Ahmed, 2008b). These showcases are few and scattered, and have yet to be absorbed by mainstream building industry (UNDP, 2011). This is a reflection of the absence of a strong coordinating framework or energy efficiency policy within the building industry (UNDP, 2011). Additionally, such showcase projects focus exclusively on new non- residential buildings.
Most research in the residential sector is focused on individual residential units, and their structural faults or indoor environmental quality (Mursib, 1999; Zakaria, 2007). Indoor environmental quality research has focused on issues such as natural day lighting, internal temperature and minimizing energy gain influenced by solar radiation (see Kubota et al., 2009; Mursib, 1999; Zakaria, 2007) but has not considered the cumulative effects of energy consumption on GHG emissions. There has been some research on the thermal performance in high rise-residential houses (Djamila et al., 2013) and comparison of comfort between modern versus traditional houses (Mursib, 1999; Zakaria, 2007).
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Recent research regarding energy performance in the residential sector included retrofitting to reduce energy consumption (Shekarchian et al., 2012) and evaluation of photovoltaic installations (Muhammad-Sukki et al., 2011). Malaysia’s solar energy potential is abundant, but solar photovoltaic applications are still mostly limited to standalone systems in the commercial and industrial sector (Mongia et al., 2007; UNDP, 2011). Key barriers in implementing and disseminating more energy efficient technology in Malaysia is largely due to the relatively low price of electricity that is highly subsidized by the government (Mongia et al., 2007; UNDP, 2011).
According to Malaysian Energy Commission, the federal government provided an average of 75% discount or subsidies in gas prices, until November 2011, in order to maintain a stable national average price of electricity at 33.54 cents per kilo Watt hour (c/kWh) (in Ringgit Malaysia – RM) (Energy Commission, 2011a). The average electricity rate for Peninsula Malaysia is 31.31 c/kWh13 for 2010 (Energy Commission, 2010). For domestic14 or residential users, the tariff rates given by National Energy Company (Tenaga Nasional Berhad – TNB) for 2012 is presented in Appendix 1.1
1.3.2 The Need for Energy Efficiency Building Codes in Malaysia
Malaysia presently has no energy efficiency strategies enforceable in the mandatory Uniform Building By-Laws (UBBL) to provide minimum energy efficiency and/or energy performance standards for buildings (Zain-Ahmed, 2008a). In addition, sectoral baseline data for energy-related GHG emissions in Malaysia is limited or at best underdeveloped (Fong et al., 2008, 2009). Presently, there is no consistent framework in Malaysia for assessing GHG emissions from buildings, which limits the development of an emissions baseline for the building sector and therefore building energy performance policies. This is reflected in the existing Malaysian Green Building Index (GBI) rating tool, which exclude any calculation for GHG emissions from buildings. The GBI rating criteria is later discussed in Chapter 3.
In reference to energy efficiency (EE) for the building sector in Malaysia, the Malaysian voluntary Standard Code of Practice on Energy Efficiency and Use of Renewable Energy
13 RM 0.31 cents is approximately equivalent to USD 0.10 cents or AUD 0.09 cents, as exchange of 12th February 2013 (XE, 2013) 14 Domestic user in this context is defined as “a consumer occupying a private dwelling, which is not used as a hotel, boarding house or used for the purpose of carrying out any form of business, trade, professional activities or services” (TNB, 2012).
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for Non-residential Buildings (MS 1525:2007) was introduced in 2005 (and updated in 2007) (Zain-Ahmed, 2008a). This voluntary code of practice is to guide effective use of energy (including renewable energy) in new and existing non-residential buildings, to reduce energy consumption within the construction, operation and maintenance of a building (Department of Standards Malaysia (SIRIM), 2004). The MS 1525:2007 is further discussed in Chapter 3. At present, a similar energy efficiency guideline for the residential sector does not exist. Therefore neither the mandatory or voluntary standards consider the impact of building energy use on climate change.
According to United Nations Development Programme (UNDP) report on Malaysia’s Building Sector Energy Efficiency Project (BSEEP)15, in 2008, Malaysia’s building sector consumed approximately 7,750 GWh of electricity and emitted 5,301 ktoe16 of GHG (UNDP, 2011). By 2009, the sector’s energy consumption increased to 8,315 GWh and its GHG emissions to 5,688 ktoe (UNDP, 2011). The increase between 2008 and 2009 was higher than expected, at a rate of approximately 7.3% for both the sector’s energy consumption and GHG emissions.
The BSEEP’s objective is to improve “energy utilization of efficiency in Malaysian buildings, particularly those in the commercial and government sectors, by promoting the energy conserving design of new buildings and by improving the energy utilization efficiency in the operation of existing buildings” (UNDP, 2011, p. 1). The BSEEP projected a business-as-usual baseline using 2008 data17 and existing policies, activities and mandates (UNDP, 2011). The forecast predicts an increase of GHG emissions to 8,088 ktoe and energy consumption to 11,824 GWh by 2014 (refer Figure 1.3). The projections scenario however, assumes that the “relative proportions of different housing types remain the same over the projection period” (UNDP, 2011 p.7), and only estimate the number of units within the residential sector, and not the floor area in its calculations (UNDP, 2011).
15 The BSEEP is an international partnership project between the Malaysian Public Works Department (PWD) with Global Environment Facility (GEF) and UNDP (Public Works Department, 2012). 16 ktoe of GHG is defined as kilo tonnes of “emission equivalents from electricity consumption using a grid emission factor of 0.684 ton CO2/MWh” (UNDP, 2011 p.5). 17 “This covers office buildings, educational facilities and hotels in both private and public sector. The information on floor space in service sector derives from the forecast presented in the Integrated Resource Planning (IRP) reference scenario, and has been adjusted to exclude shop lots and other negligible sectors. This does not include residential floor space, except those in high rise residential buildings” (UNDP, 2011 p.5).
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Therefore, any energy savings and/or emissions reduction from the residential sector is deemed indirect, as continued economic growth will likely produce larger units of housing (in terms of floor area) (UNDP, 2011). The report also pointed out that data for the current building stock was limited, especially for residential buildings (UNDP, 2011). Such findings demonstrate the need for research that provides MRV data for the low-cost housing typology and the development of a more accurate residential baseline for operational energy use and associated GHG emissions.
Figure 1.3 BAU Forecast of Annual Energy Consumption and CO2 Emissions for Malaysian Building Sector Source: UNDP (2011)
Existing government policies and legislation have been poorly formulated in dealing with energy efficiency in buildings, and efforts to incorporate the MS1525:2007 into the Uniform Building By-Laws (UBBL) have been stalled since 2003 (UNDP, 2011). The report concludes that without projects like the BSEEP, coupled with the lack of research, expertise and mandatory EE requirements, the prospect of drastically improving the energy performance of the building sector is bleak (UNDP, 2011). It is clear that the Malaysian building sector has to formulate and implement energy efficiency or energy performance standards, to help reduce its GHG emissions contribution.
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1.4 Global Context and Definition: Buildings & Climate Change
Global atmospheric greenhouse gas (GHG) concentrations have exceeded the natural range prevailing over the last 650,000 years, as a result of human activities since the industrial revolution in the 18th century (IPCC, 2007a). Increased levels of carbon dioxide (CO2) emissions from fossil fuels influences atmospheric concentrations have increased global temperatures and sea level - two marked effects of climate change (IPCC, 2007a). Carbon dioxide is one of the key greenhouse gasses contributing to global warming and causes roughly 60% of the enhanced greenhouse effect or global warming (IPCC, 2007a; UNFCCC, 1998; Watkins, 2007). The IPCC defines GHG as
“Greenhouse gases are those gaseous constituents of the atmosphere, both natural and anthropogenic, that absorb and emit radiation at specific wavelengths within the spectrum of thermal infrared radiation emitted by the Earth’s surface, the atmosphere itself, and by clouds. This property causes the
greenhouse effect. Water vapour (H2O), carbon dioxide (CO2), nitrous oxide (N2O),
methane (CH4) and ozone (O3) are the primary greenhouse gases in the Earth’s atmosphere” (IPCC, 2007c p.82)
For the purpose of consistency, this research adopts the term GHG instead of carbon emissions due to the more encompassing definition of GHG. Henceforth, ‘GHG’ emissions in this research refer to the above-mentioned definition of GHG emissions given by the IPCC.
Globally, the earth’s surface has seen an average temperature increase of 0.74° Celsius since the late 19th century and is expected to rise between 1.8 to 4° Celsius by year 2100 (UNFCCC, 2013b). Experts predict approximately 20 to 30% of flora and fauna species will be at high risk of extinction if the global temperature rises between 1.5 to 2.5° Celsius (UNFCCC, 2013b). Additionally, the global average sea level has also risen between 10 to 20 centimetre (cm) in the 20th century and is predicted to rise to approximately between 18 to 59 cm by year 2100 (UNFCCC, 2013b).
South East Asia’s (SEA) average temperature increase is calculated between 0.1 to 0.3° Celsius per decade, with sea level rising between 1 to 3 millimetres (mm) per year over the last 50 years (Jung et al., 2009). Predictions are that the region will also experience more drastic climate change and consequential impacts than the global average, with a
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forecast increased frequency and intensity of extreme weather events, damage to coastal resources, and loss of rich forest (Jung et al., 2009). By the turn of this century, SEA produced 12% of the world’s GHG emissions and its global share is expected to increase with the region’s expanding population and economies (Jung et al., 2009). This suggests a vulnerable future for the region and actions need to be taken to help mitigate climate change impact in SEA.
1.4.1 The Building Sector’s Global Impact on Climate Change
The world’s largest contributing sectors to GHG emissions between 1970 and 2004 were from energy supply, transport and industry with contributions of 25.9%, 13.1% and 19.4% of global GHG emission respectively (IPCC, 2007a). The building sector, forestry (including deforestation) and agriculture have been rising at a lower rate, contributing globally at 7.9%18, 17.4%, 13.5% respectively (IPCC, 2007a). In 2004, the building sector’s GHG emissions were approximately 10.6 GtCO2-eq/yr19, rising to approximately 30% of global GHG emissions total (Barker et al., 2007b). By 2012, this percentage has increased to approximately 40% of the global total (Levine et al., 2012), and averaging nearly 60% of global electricity consumption (Baumert et al., 2005; Comstock et al., 2012).
The building sector’s primary contribution of GHG emissions is the result of fossil fuels being used to generate electricity or used directly for building operations, in the form of fuel combustions (IEA, 2011a; UNEP-SBCI, 2009, 2010b), produces 40% of global wastes, and consumes approximately 16% of water sources (du Plessis, 2002; Sisson et al., 2009; UNEP-SBCI, 2010c). Specifically, residential buildings represent 65% of the global total sectoral emissions, and 35% for commercial buildings (IEA, 2004 cited in Baumert et al., 2005).
For example, China’s construction industry consumes almost 50% of its national energy total and produces 650 million tonnes of waste per annum (Wang et al., 2010). It is also projected that by year 2020 China will have constructed a total of 70 billion square meters (m2) of buildings, from its 40 billion m2 already constructed (in 2010) (Wang et
18 Scope of global GHG emission for the building sector is attributed to the “CO2 emissions to electricity supply rather than buildings’ end-uses. The direct energy-related carbon dioxide emissions of the building sector are about 3 Gt/yr” (Levine et al., 2007 p.389). 19 The GHG emissions calculated were inclusive of electricity use in the building sector (Barker et al., 2007b)
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al., 2010). The combined GHG emissions of India and China accounted for 51.8% of the world’s total growth of GHG emissions20 in 2008 (Anas & Timilsina, 2009; IEA, 2010).
Electricity is the main form of energy consumed in the building sector, nearly 60% of the world’s total consumption (Baumert et al., 2005; Comstock et al., 2012). Electricity consumption in commercial buildings in developed countries like the United States and Japan are driving peak demand and is expected to rise (Janda, 2009). Similarly, as developing countries raise their standards of living and services, electricity consumption in buildings is expected to exponentially (Janda, 2009). This is reflected in the increase of GHG emissions from developing countries in Asia, in the last three decades. Developing Asia21 region accounted the largest increase in GHG emission for both the residential and commercial sector, at 42% and 30% respectively (Levine et al., 2007).
Only 10-20% of building energy is consumed for pre-production and demolition or deconstruction, and similarly for its GHG emissions (Sisson et al., 2009; Urge-Vorsatz et al., 2012a). The bulk of GHG emissions from the building sector are largely produced in the operational phase (80-90%) from energy consumption for heating, cooling, lighting, ventilation and appliances (Sisson et al., 2009; Urge-Vorsatz et al., 2012a). Approximately 50% of final building energy used during operation is consumed for space heating and cooling, and between 10% to 20% is used for water heating (Urge-Vorsatz et al., 2012a).
Furthermore, the bulk of the building sector’s GHG emission comes from residential buildings, accounting for approximately 65% of the global total, while commercial buildings account for the balance of 35% (in 200022) (IEA, 2004 cited in Baumert et al., 2005). Studies suggest that without any action, the building sector ‘s energy use is expected to grow from 60% to 90% between 2005 to 2050 (Urge-Vorsatz et al., 2012a), thus increasing its GHG emissions. The building sector’s contribution to climate change can be summarized and tabulated as Table 1.4.
20 GHG emissions is calculated as CO2 emissions from fuel combustion only (IEA, 2010) 21 ‘Developing Asia’ can be defined as developing countries in Asia, which consist of China, East Asia and South Asia Afghanistan, Bangladesh, Bhutan, Brunei, Cambodia, China, Chinese Taipei, Fiji, French Polynesia, India, Indonesia, Kiribati, the Democratic People’s Republic of Korea, Laos, Macau, Malaysia, Maldives, Mongolia, Myanmar, Nepal, New Caledonia, Pakistan, Papua New Guinea, the Philippines, Samoa, Singapore, Solomon Islands, Sri Lanka, Thailand, Tonga, Vietnam and Vanuatu; as defined by the International Energy Agency (IEA, 2007). 22 Absolute emissions by the building sector in 2000 is approximately 6,418 MtCO2e (IEA, 2004 cited in Baumert et al., 2005).
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Building Sector Account for:
50% Used for space 80-90% Used heating and/or cooling during Operational 10-20% Used for water Consumes 60% of global Phase 40% Of global energy use heating electricity 10-20% Used for pre-production and demolition/ deconstruction 65% from Residential Of global GHG buildings 40% 80-90% Emitted during Operational Phase emissions 35% from Commercial buildings
Building Sector Reduction Potential:
75% Reduction in energy use 35% Reduction in GHG emissions 40% Reduction in water use 70% Reduction in waste output
Table 1.4 Building Sector’s Global Contribution to Climate Change Source: (Comstock et al., 2012; Levine et al., 2012; Urge-Vorsatz et al., 2012a)
1.4.2 The Building Sector’s Energy and GHG Emissions Scenarios
Many projects have therefore emerged in the building sector to reduce energy consumption (Kibert, 2004; Lee & Yik, 2004). It is estimated that consumption in both new and existing buildings could be reduced significantly by applying existing technologies, design, equipment, management systems and alternative solutions (Levine et al., 2007). The IPCC predicts a reduction of 75% in energy consumption for new buildings, through incorporating energy efficiency strategies in designing and operating buildings systematically (Levine et al., 2007). Holistic and systematic approaches to building systems, rather than improving individual component efficiency, is predicted to achieve significant energy reduction (Urge-Vorsatz et al., 2012a).
A recent study commissioned by the Global Building Performance Network (GBPN) estimates that by 2050 the global building final energy use can be reduced by one-third (using the deep efficiency scenario23), compared to the benchmarked 2005 values (Urge-
23 This estimate was calculated using the deep efficiency scenario, which is reflective of how “today’s state-of-the-art construction and retrofit knowledge and technologies can take the building sector in reducing energy use and CO2 emissions, while also providing full thermal comfort in buildings. In essence, we determine the techno-economic energy efficiency potentials in the building sector. In this scenario, exemplary building practices are implemented worldwide for both new and renovated buildings” (Urge-Vorsatz et al., 2012a p.25)
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Vorsatz et al., 2012a). This estimation can be disaggregated to a decrease of 34% for space heating and cooling, and 29% for water heating while assuming that full building thermal comfort is achieved worldwide (Urge-Vorsatz et al., 2012a). Additionally, this potential reduction is calculated with the expected 127% increase in floor area during the period (Urge-Vorsatz et al., 2012a).
However, calculations for a moderate efficiency scenario24 using today’s policy trends and goals show there will still be an increase in global building energy use of approximately 48% (Urge-Vorsatz et al., 2012a). However, a stark 111% increase of energy use may happen with a frozen efficiency scenario25 (Urge-Vorsatz et al., 2012a). The frozen efficiency scenario is the consequence of inaction or without policy and market developments for improved efficiency (Urge-Vorsatz et al., 2012a). These scenarios are presented in Figure 1.4.
However, it is argued that the biggest challenge to implementing more energy efficiency strategies in buildings is to create a shift in behaviour (Graham, 2008; IPCC, 2007b; WBCSD, 2008). The behavioural patterns of a building’s occupants during its operational phase play a large role in reducing (or increasing) its energy consumption and consequently its GHG emissions. Occupant behaviour in energy dependency is unlikely to shift quickly without legislation and market demand (Brown et al., 2007). Although occupant behaviour is important, other elements such as energy modelling through baseline, construction quality and other innovative building operating systems are equally as important in reducing the building sector’s environmental impact (Karjalainen & Lappalainen, 2011; Kolokotsa et al., 2011). For example, in European countries such as Austria and Germany, the housing market is being transformed with advanced technologies for energy efficiency and improved water heating (Urge-Vorsatz
24 The moderate efficiency scenario reflects the current policy trends, specifically the “implementation of Energy Building Performance Directive in the EU and building codes for new buildings in other regions. The scenario assumes an accelerated renovation rate (i.e. annually constructed buildings) to reflect that many countries recognized the importance of the quick implementation of energy-efficient retrofits and energy-efficient building codes. In all regions retrofit rates start to increase from the level of 1.4% in 2005 and reaches ‘accelerated’ levels by 2020, and stay unchanged afterwards. These ‘accelerated’ rates are different in different regions. For the key regions the following values are used: US and EI-27 - 2.1%, China – 1.6% and India – 1.5%. However, these accelerated retrofit buildings and new construction still result in far lower efficiency levels than what is achievable with state-of-the-art solutions” (Urge-Vorsatz et al., 2012a p.25-26). 25 The frozen efficiency scenario “assumes that the energy performance of new and retrofit buildings do not improve as compared to their 2005 levels and retrofit buildings consume around 10% less than standard existing buildings for space heating and cooling, while most of new buildings have higher level of energy performance than in Moderate scenario due to lower compliance with Building Codes. Retrofit rates are assumed to be constant throughout the analysed period at the level of 1.4% ... For water heating it is assumed that the fuel mix and efficiency of water heaters do not change during the analysed period” (Urge-Vorsatz et al., 2012a p.27).
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et al., 2012a). This provides an example of expanding market demand for more efficient housing in developed countries.
Figure 1.4 Energy Consumption Assessment as Compared to 2005 Values using Three Different Scenarios Source: Urge-Vorsatz, et al. (2012a)
1.4.3 Preventing the Lock-In Effect
As developing countries prepare for a growing demand for construction, it is important to invest in more energy efficient buildings and prevent the carbon ‘lock-in’ effect. Industrialized countries’ significant contribution to climate change are predominantly a result of meeting consumer’s demands for goods and services such as transportation, electricity, industrial and commercial buildings, through carbon-based energy technologies and systems (IPCC, 2007a; Unruh, 2000). According to the World Bank, in China alone it is estimated that every year lost in failure to build efficient buildings locks in approximately 800 million square meters of urban built space of inefficient energy use for decades into the future (Asia Business Council (ABC), 2007). Inefficient sectors and infrastructure prolong the operation of obsolete technologies that are highly energy dependent, which causes large-scale ‘carbon lock-in’ (Brown et al., 2007). The danger of lock-in pattern is highly relevant to climate change and environmental policies, as high GHG emissions become more difficult to reverse (Anas & Timilsina, 2009).
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The challenge now is to absorb rapidly and on a large-scale, low-carbon technologies into the economy and move beyond research and development (R&D) strategies into operation (Brown et al., 2007). The inertia to change and reduce energy-dependency manifests itself as market and policy failure that is systematically ignored, or aggravated institutionally (Unruh, 2000, 2002). Environmental policy makers need to develop policies in mainstreaming energy efficient strategies, which are based on best practice and case study energy performance.
Nevertheless, carbon lock-in is not a permanent condition, rather a persistent state that raises market and policy barriers to alternatives (Unruh, 2000). Malaysia should be strategic in implementing policies that support mainstream implementation of new technological advances to avoid or minimize the lock-in effect. Coupled with predicted energy consumption in the ‘moderate efficiency’ scenario, it appears Malaysia has to adopt stringent policies to reach its 40% emissions reduction target by 2020. Energy consumption and emissions reduction strategies in the building sector are described in the following sub-sections.
1.4.4 Pattern of End-Use Electricity Consumption in Households Worldwide
According to the World Energy Council (WEC), household electricity consumption per capita in both developed and developing regions is increasing, with the highest increase being recorded in Asia (WEC, 2010). China’s household electricity consumption is growing at an alarming rate of approximately 10% annually, while India and other Asian countries are growing at rates of between 5-6% annually from 1990 to 2008 (WEC, 2010). Conversely, Europe’s household electricity consumption is growing moderately at 0.7% annually, while North America and the Organization for Economic Co-Operation and Development (OECD) Asia and Pacific26 area are growing at between 1-2% annually, possibly due to saturation of electrical appliance ownership (WEC, 2010).
WEC estimates that the average household electricity consumption in European countries is approximately 4,000 kWh/household/year27, with OECD Asia and Pacific countries at approximately 6,000 kWh/household/year, and North America at
26 OECD Asia and Pacific or Asia Oceania comprises of Australia, Israel, Japan, Korea and New Zealand (IEA, 2011f). 27 It should be noted that the average household electricity consumption rate by the World Energy Council did not provide the number of occupants per household in its calculation, and there was no distinction between rural and urban households.
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approximately 12,500 kWh/household/year (WEC, 2010). The WEC estimate for North America is supported by the United States Energy Information Administration (EIA), which reported that the average American household energy consumption is approximately 11,280 kWh/year in 2011 (EIA, 2012). Sinha & Jenkins (2012) estimated the world average household electricity consumption at 225 kWh/month, after dividing the world total electricity consumption for the residential sector (IEA, 2011e) by the world total number of households (Dorling, 2007). This approximate calculation yields a world average of 2,700 kWh/household/year (Sinha & Jenkins, 2012) which is a much lower estimate than the average consumption in Europe, OECD Asia and Pacific countries and North America. As a comparison, WEC estimates the world average of household electricity consumption at 3,500 kWh/household/year (refer Table 1.5) (WEC, 2010).
The above estimates are a stark contrast to electricity consumption in developing countries. The household electricity consumption in India is estimated at 600 kWh/household/year, Africa at 900 kWh/household/year, and China and other Asian countries at approximately 1,000 kWh/household/year (WEC, 2010). The lower consumption rate in developing regions is likely due to less access to electricity and lower ownership of large electrical appliances such as refrigerators, washing machines and air-conditioning (WEC, 2010).
However, according to the International Energy Agency (IEA) estimates, the worldwide average household consumption is 500 kWh/household/year28 for urban households and 250 kWh/household/year for rural households (also refer Table 1.5) (IEA, 2011d). There is a significant difference from the WEC estimate of average household electricity consumption. This is probably due to the different methodologies adopted for the calculation of electricity consumption, particularly the system boundary chosen. The IEA report separates households into urban and rural areas, and assumes an average of five (5) persons per household. The WEC reports do not define an average household occupancy, nor does it aggregate electricity consumption into rural and urban households. Consequently, a comparison of average household electricity consumption between WEC and IEA studies is difficult to carry out because of these differences in reporting methods.
28 The IEA (2011) report assumes an average of five (5) people per household
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Alternatively, modelling of energy performance at the building scale, such as the Global Buildings Performance Network’s (GBPN) estimates note the average urban household29 in North America consumes approximately 190 kWh/m2/year, while Europe’s average consumption is 170 kWh/m2/year (also refer Table 1.5) (Urge-Vorsatz et al., 2012b). The Building Performance Institute Europe (BPIE) also studied Europe’s household energy consumption for space heating in 2010. Space heating is the dominant energy end-use at approximately 70% of the total. Germany’s average was approximated at 53 kWh/m2, Portugal at 68 kWh/m2, and the United Kingdom at 102.8 kWh/m2 (Economidou et al., 2011).
In contrast, China and India’s average household electricity consumption is estimated at 60 kWh/m2/year and 75 kWh/m2/year respectively (also refer Table 1.5) (Urge-Vorsatz et al., 2012b). The GPBN report however did not provide estimates for other developing Asia countries, or an estimate for the world average. Similarly, the WBCSD’s report estimates average household energy consumption for the United States of America (U.S.A) based on two typologies, apartment and detached homes at 212 kWh/m2/year and 123 kWh/m2/year, respectively (also refer Table 1.5) (WBCSD, 2009). UNEP-SBCI also provided case studies report on their Common Carbon Metric Phase I Pilot Project, which reported the performance level of a city that included residential buildings30 to consume approximately 52 kWh/m2/year and 3,734 kWh/occupant/year (UNEP-SBCI, 2010d).
29 The GBPN figures reflect urban multi-family (MF) electricity consumption for 2005. The GBPN report used the Building Performance Institute Europe (BPIE) building classification for Europe, whereas regional experts’ assumptions were used for India, China, and the North America (Urge-Vorsatz et al., 2012a). It should also be noted that the GBPN’s methodology did not adopt the CCM as its reporting tool. 30 The UNEP-SBCI (2010d) report states that for this case study (City A) was analyzed using a top-down approach, which had an area of 176 km2 and an occupancy of 3,700,000 people (UNEP-SBCI, 2010d).
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Energy Consumption Metrics Source of Performance Metrics kWh/m2/yr kWh/occupant/yr kWh/household/yr
Ariffin (2012) 7,789 (Medium Density)
Noordin (2012)31 - - 3,012 (Malaysian Average)
TNB (1999) cited in Tang (2005) - - 2,754 (Malaysia Average)
Tang (2005) - - 2,200 (Malaysia Average)
190 (North America) 170 (Europe) GPBN (2012) - - 60 (China) 75 (India)
Sinha & Jenkins (2012) - - 2,700
EIA (2011) - - 11,280 (U.S.A) 550 (Urban - Worldwide) IEA (2011) - - 250 (Rural - Worldwide) 53 (Germany) BPIE (2011) 68 (Portugal) 102.8 (UK)
WBCSD32 Apartment 212 7,740 15,760 (U.S.A) (2009) Detached home 126 11,630 31,730 (U.S.A)
UNEP-SBCI (2010d) 52 3,734 -
3,500 (Worldwide)
4,000 (Europe)
6,000 (OECD Asia & Pacific)
WEC (2010) - - 12,500 (North America)
1000 (China)
600 (India)
900 (Africa)
Table 1.5 Average Household Energy Consumption Source: (Economidou et al., 2011; IEA, 2011d; Tang, 2005; UNEP-SBCI, 2010d, 2011; Urge-Vorsatz et al., 2012b; WBCSD, 2009; WEC, 2010)
31 It must be noted that the average data for household GHG emissions used in this research is based on a household unit consisting of 4.31 persons, according to the 2010 Population and Housing Census (Department of Statistic Malaysia, 2011; Noordin, 2012). 32 The estimations are for average household in United States (US) for 2005, with an estimated household size of 2.57 persons (OECD, 2011).
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Research on Malaysian average household electricity consumption has been done previously by Tang (2005), Noordin (2012) and Ariffin (2012). Tang (2005) estimated that the Malaysian average household electricity consumption was approximately 2,200 kWh/household/year (Tang, 2005) in 2005 , while Noordin (2012) estimated it to be 3,012 kWh/household/year (Noordin, 2012) in 2011. However, Ariffin (2012) calculated the average Malaysian typical medium density house consumes approximately 7,789 kWh/household/year (Ariffin, 2012) (also refer Table 1.5).
1.4.5 Energy and Emissions Reduction Strategies in the Building Sector
It is evident that the building sector’s energy consumption and GHG emissions not only play a significant role in climate change, but are potentially set to rise. There is an urgent need to change how the buildings are built, operated and maintained in order to reduce their environmental impact. According to the IPCC’s Fourth Assessment Report, measures to reduce GHG emissions from buildings can be implemented by any of the following:
1. Reducing both energy consumption and embodied energy in buildings; 2. Switching to lower carbon fuels as well as achieving a higher share of renewable energy; or
3. Controlling the emissions of non-CO2 GHG gases (Levine et al., 2007, p.389).
Monitoring the performance level of buildings also facilitates identification of opportunities to improve operating efficiency in the long run (Levine et al., 2012). Actual building performance depends on the quality of construction, design, operation and maintenance systems (Janda, 2008; Jennings et al., 2011). Measuring the operating performance of buildings will not only identify unsustainable patterns of construction and consumption, but also identify energy inefficiencies and GHG mitigation possibilities (Levine et al., 2007). Actual building performance can also be used to establish a performance baseline based on stock aggregation, in terms of energy performance (in kWh/yr) and GHG emission (in tCO2e./yr) (UNEP-SBCI, 2010b). Discussion on energy and emissions reduction strategies is further presented in Chapter 2.
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1.4.6 Policy Development and Energy Efficiency in Buildings
Policy development is often regarded as a process, from a “sequence of political events that result in important policy outcomes” (Ellefson, 2000 p.82). Policy efforts made by governments to reduce building energy consumption in industrialized and developing countries in the past three decades can be categorized by economic incentives, informational programmes, or regulatory requirements (Janda, 2009; Levine et al., 2012). Economic incentives include tax cuts and energy pricing; while informational programmes include initiatives like energy awareness campaigns and energy audits (Janda, 2009; Levine et al., 2012). Finally, regulatory requirements include energy efficiency or energy performance building codes and standards, energy labelling, appliance standards, and procurement regulations (Janda, 2009; Koeppel & Urge- Vorstaz, 2007; Levine et al., 2012; UNEP, 2007).
Conversely, some have suggested a voluntary-based approach, as opposed to mandatory regulations, is more effective because it offers greater flexibility for stakeholders to achieve a target (Lee & Yik, 2002, 2004; Peterman et al., 2012). Voluntary initiatives such as electing to follow an energy efficiency building code and financial incentives for efficient technology are more aimed at raising awareness and influencing behavioural characteristics among professionals (Lee & Yik, 2002, 2004). However, studies show that despite the arguments for voluntary approaches, enforcement remains an issue, especially in developing countries (Birner & Martinot, 2002; Deringer et al., 2004; Koeppel & Urge-Vorstaz, 2007).
In Asia, the level of implementation of these policies are uneven and are largely dependent on governmental efforts (Hong et al., 2007; OCEAN, 2009). For example, in South East Asia (SEA), mandatory energy efficiency building codes have been introduced in Indonesia, The Philippines, Singapore, Thailand and Vietnam (OCEAN, 2009; UNEP & Building and Construction Authority Singapore (BCA), 2011; United Nations Economic and Social Commission for Asia and the Pacific (UNESCAP), 2010). However, the Philippines National Building Code has only included energy efficiency as a voluntary requirement, amidst its mandatory sections (OCEAN, 2009). Similarly, Thailand’s EEBC requirement is only applicable to commercial and government buildings, while Vietnam’s Energy Efficiency Commercial Code has not yet been widely disseminated (OCEAN, 2009).
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This is in contrast to the strictly enforced Minimum Energy Performance Standards and Building Control (Environmental Sustainability) Regulations in Singapore (OCEAN, 2009). Other countries like Brunei, Cambodia, Lao People’s Democratic Republic (PDR), Malaysia and Myanmar have yet to implement mandatory energy efficiency building codes, but some have introduced voluntary energy efficiency guidelines, conservation programmes, or codes for non-residential buildings (OCEAN, 2009; UNEP & BCA, 2011; UNESCAP, 2010). A more detailed description of SEA’s energy efficiency building codes is presented in Chapter 2. Consequently, in identifying the problem context and rationales, the research objectives and questions are presented next.
1.5 Research Objectives and Questions
The preceding discussion clearly show the knowledge gap in energy-related GHG emissions baseline data for the public low-cost housing typology, development of energy efficiency legislation for the building sector, and in the current definition of low-cost housing that excludes operational costs so long-term affordability is uncertain. The overall aim of this research is therefore to fill this gap by providing MRV data from public low-cost housing projects in Malaysia to support the development of building energy efficiency (BEE) policy. The following objectives have been derived to fill the knowledge gaps identified above:
1) to measure energy performance and operational GHG emission using the international Common Carbon Metric in the Malaysian context; 2) to investigate the end-use electricity consumption pattern of households; and 3) to investigate percentage of household income spent on rent, electricity and other utilities, to examine operational affordability.
The research addresses these objectives by collecting electricity consumption data, implementing the CCM to generate baselines for energy and GHG emissions, and investigating end-use patterns and affordability through a survey of case-study buildings. Employing a top-down and bottom-up analysis of selected People’s Housing Programme – Program Perumahan Rakyat (PPR) low-cost housing projects as emblematic case studies, produces output that contribute to forming a national emissions database for low-income housing. It provides the tangible data needed to inform policy and performance-based building code development.
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The bottom-up approach requires an investigation of actual building stocks, either individually or in groups of buildings (UNEP-SBCI, 2010). The top-down approach requires gross energy data and building stock profile data of the ‘whole’, either region, city or national level (UNEP-SBCI, 2010). The CCM employs both bottom-up and top- down approaches which is applied in the case study methodology. There are two parts to the case study, 1) is to collect electricity bills, and 2) to conduct a survey questionnaire. Detailed description of the case study methodology is explained in Chapter 5. The following research questions have been formulated to realize the objectives:
1) How can GHG baseline emission data influence policy development related to energy efficiency in the Malaysian building sector? a. What are the different types of baselines and how does it influence policy? b. What are the key energy efficiency and emissions related issues affecting the Malaysian building sector? c. What energy-related policies are currently implemented in the Malaysian building sector? d. What indicators can be used to inform policy makers in developing energy efficiency policies for the Malaysian building sector?
2) What is the operational energy performance and related GHG emissions level of public low-cost housing in Malaysia? a. How should low-cost housing be assessed for its energy performance? b. What is the operational energy-related GHG emission baseline of this building type?
3) What is end-use electricity consumption patterns and operational affordability in low-cost housing? a. What is the most appropriate method of investigation and what are the indicators? b. What is the average hourly daily operating time of electrical appliances in the average household? c. What are the significant contributors to end-use electricity consumption in the average household? d. What is the operational affordability for low-income households in the PPR low- cost housing?
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A summary of the research design and methodology employed to investigate these questions, along with the thesis structure; its limitations and implications are presented in the following section.
1.6 Research Design
A case study approach has been adopted to investigate the operating energy performance of low-cost housing projects and measure their GHG emissions using the UNEP-SBCI Common Carbon Metric (CCM). Case study methodology relies on various sources of evidence to converge in triangulation analyses, while deliberately covering the contextual conditions (Johannson, 2003; Yin, 2003). This is one of the strengths of a case study methodology, whereby different methods are combined and triangulated to analyse the case from different angles in order to strengthen and corroborate the findings (Johannson, 2003; Noor, 2008).
Case studies present an in-depth insight into issues and range from collecting evidence to redrawing a generalization (Aziz, 2007). Figure 1.5 briefly illustrates the various sources of information or evidence for the case study methodology adopted in this research. The case study adopted two types of investigation to acquire both quantitative and qualitative evidence and information, i.e. a physical investigation (or field work) of selected low-cost housing projects and a literature investigation of energy efficiency standards and energy data for Malaysia.
Figure 1.5 Multiple Sources of Evidence Adapted from Yin (2003)
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Investigating the real-life context of GHG emissions from building operation requires an understanding of the relationship between occupant behaviour and energy consumption. A structured interview (or questionnaire survey) investigating how households utilize electricity is required in order to analyse this relationship and the affordability of electricity. Therefore, the analytical framework is constructed to triangulate data from multiple sources, in order to reach the research objectives. The research is also designed to be flexible, considering the location and time constraints in conducting field work and gathering local data. As the research’s objectives are to evaluate the energy related- GHG emissions of low-cost housing in Malaysia, it is necessary.
The pilot study indicated that electricity bills from the national energy provider company (Tenaga Nasional Berhad) were accessible. Furthermore, by calculating carbon emissions through electricity bills for 2010 to 2011, it was possible to gain an overview of the yearly GHG emissions of low-cost housing projects. Calculating GHG emission through operational electricity consumption provides clear MRV indicators for assessment and provides an insight to the contemporary building stock’s environmental performance.
Furthermore, measuring GHG emissions of existing government low-cost housing builds up data and is likely to act as a potential benchmark for future low-cost housing development. Government low-cost housing is not only the major typology, but also has the biggest potential to implement policy and change construction guidelines as the government wholly controls it. The research also investigates the affordability of electricity in public low-cost housing projects. This is achieved by analysing the percentage of monthly household expenditure to electricity bills, cross referenced through the structured interviews (or questionnaire survey) and utilized electricity bills. Using the CCM to measure operational GHG emissions on a sample emblematic case study and a survey questionnaire provides a more generalizable insight into public low- cost housing emissions in Malaysia.
This research also helps to validate the implementation of an international tool for generating MRV energy and emissions baselines for application to the local Malaysian public low-cost housing context and investigates its limitations and implications. Henceforth, this research provides the evidence and data needed to encourage policy
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development on energy efficiency in residential buildings in Malaysia, not just affecting the public low-cost housing sector but other residential typologies.
1.7 Thesis Structure
The research first begins with a literature review to contextualize the basis of the argument and define the environmental performance of buildings in relation to operational GHG emissions. The overall thesis structure, set to answer the research questions, is illustrated in Figure 1.6. The research objectives, questions and research design are explained in Chapter 1. Chapter 1 also highlights the vast gap in knowledge, in terms of environmental research for the Malaysian public low-cost housing typology and the need for MRV data to help generate an energy consumption and GHG emissions baseline for the residential sector. This Chapter also presents building sector impacts climate change globally and provides examples of energy efficiency standards and policies that are implemented internationally. Chapter 1 also critically discusses limitations of this research and identifies areas for further research.
Chapter 2 starts with highlight the need for an energy efficiency building code in Malaysia, as no mandatory guidelines are currently implemented. This Chapter also briefly presents available voluntary methods and energy efficiency guideline that are available for the Malaysian building sector, like the Green Building Index rating tool and the Malaysian Standard Code of Practice on Energy Efficiency and the Use of Renewable Energy for Non-residential Buildings (MS 1525:2007). Consequently, there is a brief description of current energy efficiency legislation and policies available in South East Asian countries, in comparing Malaysia to its neighbouring counterparts and what policies can be applicable. Following that, the Chapter explains the need for baseline studies in order to inform policy development and succinctly describes the different types of baseline. Chapter 2 also highlights the knowledge gap in existing assessment methods to measure the building sector’s climate impact, where most tools focus on simulation data rather than measured performance. This research is addressing this knowledge gap by collecting measured and reported data of utilized electricity bills, in order to inform an energy consumption and GHG emissions baseline for the Malaysian residential sector and appropriately using the UNEP-SBCI’s Common Carbon Metric (CCM).
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Chapter 3 describes and justifies the case study context, in terms of using the electricity as the main indicator for household energy consumption and developing a project- specific baseline using the CCM. This Chapter also explains why operational affordability is highly relevant in the context of energy poverty and the energy rebound effect. Chapter 3 also briefly explores the chronological development of energy and environmental policy in Malaysia, and highlighting the direction of future policy development.
The case study methodology explained is in Chapter 4 presents a contemporary insight into real-life situations by gathering evidence from various sources and fieldwork investigations to converge in triangulation analyses. The three (3) areas of triangulation are:
1) Literature review; 2) Quantitative analysis of electricity bills; and 3) Qualitative analysis of survey responses.
A survey questionnaire is also adopted for the case study in investigating the affordability of electricity for households in public low-cost housing projects. Using the Common Carbon Metric, the case study adopts both a top-down and bottom-up approach to investigate the environmental performance of low-cost housing projects in Malaysia. Chapter 4 identifies also describes the case study’s systems boundary, in terms of energy, location, building typology and building life-cycle boundaries. The pilot case study is also described in Chapter 4.
Presented in Chapter 5 is a step-by-step guideline in using the UNEP-SBCI’s Common Carbon Metric to generate an operational energy-related GHG emission baseline. Chapter 5 explains in detail the case study protocol for the CCM’s Top-Down and Bottom-Up Approaches. The survey questionnaire protocol is also described in this Chapter, separated by three main sections of the questionnaire: basic demographics, average hourly consumption of electrical appliance, and average operational household expenses for rent, electricity and other utilities.
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Figure 1.6 Thesis Structure
Chapter 6 illustrates the findings of fieldwork investigations and calculates the operational carbon equivalent emissions related to energy for the selected low-cost housing projects. The findings in Chapter 6 are categorized by each Top-Down and Bottom-Up approaches, and the discrepancies by both approaches are discussed at length. The main reason for such discrepancy is due to different systems boundary adopted by the Top-Down and Bottom-Up approaches.
Next, Chapter 7 synthesizes and generalizes all the data collected from the survey questionnaire and previous chapters of the literature review. Chapter 7 summarizes the
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end-use pattern on electricity consumption based on hourly consumption of selected household electrical appliances, and the operational affordability for the average PPR household.
Chapter 8 summarizes and reprises the research questions and draws conclusions on implications for the Malaysian context and the wider body of knowledge. Chapter 8 also recaps the limitations of research that was encountered during three stages: 1) determining the systems boundary and designing the case study protocol, 2) conducting the fieldwork, and 3) analysing the data. Finally, the thesis ends with future research recommendations and its contribution to knowledge.
1.8 Limitations of Research
Housing clearly is a complex subject that deals with matters of income or economic, social, environmental and cultural boundaries, and most of the time is must be studied holistically. However the scope of this research is focused on providing measurable, reportable and verifiable data of energy-related performance of public low-cost housing in Malaysia, using low-cost housing projects in Kuala Lumpur as a case study. The findings and conclusions of this research are therefore based on context specific case studies, focused on one building typology and climate zone. While the low-cost housing typology is common across Malaysia, the limitations of the field-work sites have been considered when drawing conclusions.
Although investigating the energy and carbon intensity of low-cost housing projects does not address holistic triple-bottom-line sustainability performance assessment, it does provide a substantial understanding of environmental building performance. Using the CCM in calculating indirect carbon emissions is based on measured consumption of electricity noted on electricity bills from individual units or households within the low- cost housing project. These electricity bills over a minimum duration of one (1) year are needed, in order for the CCM to generate carbon emission data.
Electricity bills for individual households or units are private and can be considered the confidential property of the proprietor. It would be very difficult to collect all the electricity bills from individual units in the case study low-cost housing project. Consequently, these electricity bills must be obtained from the electricity provider
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company on a confidential basis. This opportunity is subject to the approval and cooperation of the electricity provider company, Tenaga Nasional Berhad. The fieldwork was limited to a three (3) month period, all preparations where concluded prior to the field work and a pilot study was done beforehand, in order to help structure the data collection and analysis protocols.
In relation to producing measurable, reportable and verifiable (MRV) findings, the research is limited in verifying the findings as the electricity data collected are only verifiable through apportionment of electricity consumed by the low-cost housing typology against the national energy consumption for the residential sector. The verification of electricity consumption needs to be further investigated, as findings from the CCM produced two separate analyses, through its two approaches (top-down and bottom-up).
Methods such as in-situ measurement of electricity consumption by simple power meters could have been used to verify such electricity data. However such method can be considered quite intrusive to a household’s privacy and would consume lengthy periods of time to collect in-situ measurements on individual electricity power meters. Additionally, as survey questionnaire and collection of utilized electricity bills from TNB needed to be conducted concurrently during the limited fieldwork period, the sample size for this study was too big (266 household units) for the researcher to carry out individually.
Therefore this method was considered unsuitable for the limited time available for fieldwork, and in order to maintain anonymity of data. Similarly, internal temperature measurements could help cross-relate household’s electricity consumption in terms of cooling load needed to maintain a comfortable indoor temperature. This method was also considered not suitable for the same reasons of using an in-situ electricity power meter. Additionally, the utilized electricity bills were not collated with the households surveyed, cross-referencing the electricity consumption based on air-conditioning system could not be determined. Other limitations on the CCM are further discussed in Chapter 6.
Some limitations were identified when conducting a survey questionnaire, as the household income and expenditure data are only an indication of real life expenses, as it
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is based on self-reported data and not actual household expenditure accounts or national census. No further validation of the questionnaire findings were conducted, and should be highlighted as recommendations for future research. There are also risks when conducting and analysing survey questionnaire, such as human error in data entry during the questionnaire execution stage and in the analysis stage. Adopting statistical principles, such as confidence level and sampling error or confidence interval, in the sampling techniques, reduces these errors. These statistical principles are further discusses in Chapter 4.
It is also recognized that this research only contributes to a segment of knowledge in understanding the environmental performance of low-cost housing typology by measuring GHG emissions of sample units. Other environmental and social characteristics that are also important, such as building materials, waste management, public amenities and so forth, lie beyond the scope of this thesis.
1.9 Implications
This chapter has introduced the context and rationale of research, together with the thesis structure and research design. This sets the scene for a more detailed argument of the energy related performance of low-cost housing projects in Malaysia, which is further elaborated in Chapter 3. The subject of low-cost sustainable housing is still somewhat uncharted territory in Malaysia and this research is aimed at exploring the possibilities of dealing with climate change and GHG mitigation issues in the Malaysian housing sector.
This research project’s contribution to knowledge is presenting actual data of energy consumption and related GHG emissions for the low-cost housing typology in Malaysia. This research will present measurable, reportable and verifiable (MRV) data of energy consumption in kWh/m2/year or kWh/occupant/year, and indirect GHG emissions in kgCO2e./m2/year or kgCO2e./occupant/year, in informing policy makers. Furthermore, investigating the end-use consumption of electricity in average low-income households will help indicate which areas of policy to focus on in terms of energy efficiency building codes for the residential sector. Consequently, recommendations relating to housing policy can be made by measuring the affordability of electricity and other household expenditure in the average low-cost housing households.
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However, monitoring the impact of such strategies over time requires baselines of building performance to be established. In fact, findings of emission trends research in Malaysia conclude that the country lacks a consistent sectoral GHG database (Fong et al., 2008, 2009). Accordingly, the need for Malaysia to develop baseline studies in setting a benchmark and monitoring its emission reductions is necessary. This also falls in line with Malaysia’s voluntary pledge to a 40% carbon reduction by 2020 compared to the 2005 level of emissions. It is hoped that this research can be adapted to similar contexts within the building sector and under similar climatic conditions.
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Chapter 2: Buildings & Climate Change. The Need for Building Energy & Mitigation Policy in Malaysia
2.1 Introduction
The Malaysian Standard Code of Practice on Energy Efficiency and the Use of Renewable Energy for Non-residential Buildings (MS 1525:2007) was introduced in 2001 by the Standards and Industrial Research Institute of Malaysia (SIRIM), and revised in 2007. Its aim is to guide the effective use of energy (including renewable energy) in new and existing non-residential buildings (SIRIM, 2004; Zain-Ahmed, 2008a). MS 1525:2007 is a voluntary guideline to “encourage the design, construction, operation and maintenance of new and existing buildings in a manner that reduces the use of energy without constraining creativity in design, building function and the comfort or productivity of the occupants, and appropriately dealing with cost considerations” (SIRIM, 2007 p.1).
The MS 1525:2007 recommends an annual energy consumption rate for non-residential buildings at 135 kWh/m2/year (Shafii, 2008; SIRIM, 2007; Zain-Ahmed, 2008b). This energy consumption recommendation can be used to establish a baseline for non- residential buildings in Malaysia. However, similar energy efficiency or energy performance standard for the Malaysian residential sector does not exist (SIRIM, 2004; Zain-Ahmed, 2008a), therefore energy consumption baseline for residential buildings have yet to be established. This research helps to fill in the gap in establishing an energy consumption baseline for residential buildings in Malaysia.
Energy efficiency for residential buildings in Malaysia is neither regulated nor promoted (UNDP, 2011), which is likely to have significant implications for its energy end-use performance (APEC, 2011b). Without such legislation to reduce the sector’s energy consumption, its GHG emissions growth is inevitable and puts the country at high risk for carbon lock-in with more inefficient buildings being constructed. Energy efficiency performance standards would help reduce total GHG emissions from electricity consumed by the building sector. It is also crucial for stakeholders in the building industry to promote existing guidelines to reduce its overall environmental impact.
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The Malaysian construction industry has always played an important role in the country’s economic and social development, providing approximately 8% of total workforce annually (Abdulllah et al., 2004; CIDB, 2007a). In 2011, the Malaysian construction industry contributed approximately 3% to the national Gross Domestic Product (GDP), with an annual growth of 4.6% (Department of Statistics, 2011b). However, industry must be able to change and expand innovatively, in order to meet shifting demands and growing international standards (Abdulllah et al., 2004; Hamid & Kamar, 2010).
The Malaysian construction industry has yet to streamline and modernize its approach to innovative building systems and energy efficiency (Hamid & Kamar, 2010). For example, the Construction Industry Development Board (CIDB) missed an opportunity to promote energy efficiency in the Construction Industry Master Plan (2006-2015) (refer Appendix 3.1), which was launched in 2007 (CIDB, 2007b). Poor quality of construction, maintenance and performance of contractors remain the central challenges affecting the industry (EPU, 2010; Hamid & Kamar, 2010). Most environmental problems in Malaysia are caused by “lack of environmental considerations in the exploitation, development and management of resources as well as lack of control of pollution resources” (Hussein & Hamid, 2008, p. 4).
2.2 Lack of Environmental Guidance in National Housing Policy
A National Housing Policy (NHP) was recently introduced in 2011 as a guideline to provide adequate quality and affordable housing to all relevant stakeholders at the federal, state, local and private sector levels (National Housing Department, 2011). One of the National Housing Policy’s objectives is to set the “future direction to ensure the sustainability of the sector” (National Housing Department, 2011 p.76). Two other objectives set in the housing policy are to provide “adequate and quality housing with comprehensive facilities and a conducive (living) environment” and to enhance “the capability and accessibility of the people to own or rent houses” (National Housing Department, 2011 p.76).
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Also included in the NHP, are plans to invest RM500 million33 for maintenance and repair works for both public and private low-cost housing projects (National Housing Department, 2011). This fund is disbursed on a matching grant basis, whereby the government will bear half the cost, while the other half is borne by housing management committees or residents’ association (National Housing Department, 2011). In order to reach the aim and objectives of the NHP, six (6) strategic thrusts were identified (National Housing Department, 2011 p.79):
1) Provision of adequate housing based on specific needs of target groups; 2) Improving the quality and productivity of housing development; 3) Increasing the effectiveness of implementation and ensuring compliance for the housing service delivery system; 4) Improving the capability of the people to own and rent houses; 5) Sustainability of the housing sector; and 6) Enhancing the level of social amenities, basic services and liveable environment..
The currently low construction quality of housing is recognized in the NHP as a major challenge to the industry (National Housing Department, 2011), as many developments still do not meet the minimum standard requirements (EPU, 2010). This is due to weaknesses in implementation of the building regulations and enforcing related legislation. Poor quality is also highly dependent on unskilled and cheap foreign labour (National Housing Department, 2011). Hamzah (2012) recently revealed that there are numerous overlapping requirements in Malaysia’s legislation, for instance zoning provisions which fall under both the National Land Code and the Town and Country Planning Act, has caused a non-standard requirement that is addressed on a case-by- case basis. This flexibility and non-standard procedure is consequently perceived by housing developers as manipulation of the regulations that could lead to corruption (Hamzah, 2012).
Hamzah (2012) also presented a list of all Federal regulation that affected low-cost housing provision in Malaysia (refer Table 2.1), and proposed that any low-cost housing project that exceeds the minimum 50 acres development area, be required to provide Environmental Impact Assessment (EIA) (Hamzah, 2012). However, there is a clear
33 RM 500 million is approximately equivalent to AUD 154 million or USD 161 million. Exchange rate as of 3rd February 2013 (XE, 2013).
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environmental oversight within the newly developed National Housing Policy, as it mainly focuses on social and construction quality issues. This gap needs to be addressed if the Malaysian building sector wants to compete globally, as most developed countries have also implemented energy efficiency and performance standards for both residential and non-residential sectors.
Area of Control Federal Regulations Significance to Low-Cost Housing (LCH) Provision
Administration Housing Development Act Outlining obligations of private developers to provide LCH (Developer) 1966 through licensing conditions. Protect buyers/owners through the standard contract between developer and buyer (Sale & Purchase Agreement) – Schedules G and H. Administration Local Government Act Outlining the functions, rights and obligations of the Local (Local Authority) 1976 Authority. Describes power of State Authority over Local Authority for direction and law-making. Land National Land Code 1965 Controls land ownership, land use and dealings. Development Prevents speculative activities for LCH by inserting “Restriction in interest” (written consent by State Authority in land dealings) and “Express conditions” (land to accommodate only LCH). Planning Town and Country Requires planning permission for land development Planning Act 1976 activities, including land sub-division. Building Code Street, Drainage and Outlines minimum standards for LCH housing for health, Building Act 1976 safety and welfare purposes. Uniform Building By- Requires the principal submitting person (architect, Laws 1984 engineer or draughtsman) to monitor and certify the Fire Services Act 1988 completion and compliance (Certificate of Completion & Sewerage Services Act Compliance-CCC) of its construction 1993 The CIS prescribes minimum requirements on layout, Construction Industry space and configuration of individual units Standard (CIS) 1 and 2
Environmental Environmental Quality Act Requires an Environmental Impact Assessment (IEA) Considerations 1974 report to be submitted for proposed housing developments of more than 50 acres. Strata Property Strata Titles Act 1985 Controls ownership, dealings, construction and Strata Management maintenance and management of LCH. Act 2013
Table 2.1 Summary of Federal regulations affecting low-cost housing provision in Malaysia Source: Adapted from Hamzah (2012)
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2.2.1 Energy Efficiency Voluntary Measures and Incentives
The MS 1525:2007 stipulates energy efficiency standards and recommendations for renewable energy application for new non-residential buildings and retrofit of existing buildings (SIRIM, 2007). EE requirements made in the MS 1525:2007 are such as efficient lighting systems, efficient air-conditioning and mechanical ventilation systems, and designing an energy management system (SIRIM, 2007). Recommendations for renewable energy emphasis of strategies such as (SIRIM, 2007 p.1): a) Maximizing the availability of renewable energy resources such as solar heating, solar electricity, solar lighting and solar assisted technologies; b) Optimizing passive cooling strategies; c) Optimizing environmental cooling through natural means such as vegetation, site planning, landscaping and shading; and d) Maximizing passive solar design.
The scope of the MS 1525:2007 guideline is divided into seven categories: architectural and passive design strategy, building envelope, lighting, electrical power and distribution, air-conditioning and mechanical ventilation (ACMV) system, an energy management control system, and building energy simulation method (SIRIM, 2007). Estimated energy consumption for a typical non-residential building in Malaysia is broken down to 52% for air conditioning, 20% for lighting and 28% for other equipment (Kristensen, 2003 cited in Shafii, 2008 p.3). As the average non-residential building in Malaysia consumes between 250-300 kWh/m2/year, it implies that more drastic strategies are needed to comply with the energy efficiency guideline.
Localized climatic design strategy can be seen in the architectural and passive design strategy and building envelope categories, which combines architectural, engineering, site planning and landscaping multidisciplinary approach in designing a more energy efficient building (SIRIM, 2007). The architectural and passive design strategies include site planning and orientation, natural day-lighting, natural ventilation, façade design and material, and strategic landscaping (SIRIM, 2007). Building envelope category stipulates minimum standards for OTTV, shading co-efficiency, day-lighting, maximum thermal transmittance (U-value) for roofs and RTTV for air-conditioned buildings, and air leakages (SIRIM, 2007). A similar code of practice for residential buildings is absent.
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The Malaysian building sector is regulated by the Uniform Building By-Law (UBBL) that prescribes minimum specifications for features such as ventilation, structural and constructional requirements, and fire safety (APEC, 2011b; Ministry of Finance, 2006). The by-law, however, has yet to include energy performance or energy efficiency guidelines (UNEP & BCA, 2011). However, there are plans to incorporate MS 1525:2007 into the UBBL by 2015 under the National Energy Efficiency Master Plan (NEEMP), an initiative by the Ministry of Energy, Green Technology and Water (MEGTW) (APEC, 2011b). This effort however is yet to materialize (APEC, 2011b; UNDP, 2011).
Other governmental initiatives such as the Malaysian Industry Energy Efficiency Improvement Programme (MIEEIP) developed by MEGTW, is aimed at improving energy efficiency in the industrial sector (UNEP & BCA, 2011). The MIEEIP objectives included removing barriers to improving the efficiency of industrial energy consumption, generating an institutional sustainability capacity, and developing a conducive policy, planning and research framework. The Sustainability Achieved via Energy Efficiency (SAVE) programme was also initiated by MEGTW to provide consumer rebates on identified appliances such as energy efficient refrigerators, air-conditioners and chillers. Tax exemptions are also available for buildings that have Green Building Index certification (UNEP & BCA, 2011).
Additionally, MEGTW have been promoting energy efficiency in government-owned buildings, as presented in Chapter 1. In 2006, an energy audit conducted on the Low Energy Office (LEO) building calculated its energy consumption at 104 kWh/m2/year, which subsequently won the ASEAN Building Energy Award (MEGTW, 2009; Shafii, 2007). Other buildings like Malaysian Energy Centre’s Zero Energy Building in Bangi (ZERO) and Energy Commission’s Diamond Building in Putrajaya were designed to reduce energy consumption to 50 kWh/m2/year and 85 kWh/m2/year, respectively (Zain- Ahmed, 2008b).
In 2007, the Malaysia Green Building Council (now known as the Malaysia Green Building Confederation – MGBC) was formed after a concerted effort to guide the building industry towards more sustainable solutions (MGBC, 2011). MGBC’s main objective is “to support the government in developing a sustainable built environment” and “to facilitate exchange of knowledge among different stakeholders in the building and construction industry” (MGBC, 2011, Goals & Objectives section, para. 5). Since
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then, more investment into advancing technological construction and building material research were implemented to address environmental issues within the building sector (see Shafii, 2007).
Notwithstanding the measures and efforts already in place, the most critical gap still lies in the lack of energy efficiency or energy standards for residential buildings in Malaysia. Even more so, there is lack of energy efficiency or conservation measures for existing residential buildings in Malaysia. This is also reflected in the Malaysian Green Building Index (GBI), which excludes existing residential buildings in its assessment.
2.2.2 The Malaysian Green Building Council and Green Building Index
The GBI currently only applies to non-residential buildings (existing and new), residential buildings (new only), industrial (new and existing), and newly included townships34 (Greenbuildingindex, 2012b). The GBI remains a voluntary tool and has yet to introduce the rating tool for existing residential buildings. This presents an apparent gap in research practice and the need for policy development, particularly for existing residential buildings, in terms of energy efficiency or energy performance standard for building operations.
The GBI Residential certification presents a general scorecard based on a point-system calculation that measures the relevant design features. This certification, which is not administrated by the government, does not imply any energy standard nor does it ensure best practice on energy efficiency. The GBI’s energy efficient (EE) assessment criteria for new residential buildings are divided into five categories, i.e. minimum energy performance, renewable energy, advanced energy efficiency performance based on Overall Thermal Transfer Value (OTTV) and Roof Thermal Transfer Value (RTTV), home office and connectivity, and sustainable maintenance. The minimum EE performance criteria is based on OTTV and RTTV that is adopted in the MS 1525:2007, which sets a minimum standard of less than 50 Watts per meter square (OTTV ≤ 50
34 The GBI’s defines sustainable township as “livable places that meet the diverse needs of the community. They are places that are well planned and designed, safe and secure, and enhances the surrounding environment, thus providing a high quality of life for the people who live, work and play there” (Greenbuildingindex, 2012a, p. 8). The township category is assessed by six (6) criteria, namely climate, energy and water (CEW), ecology and environment (EEC), community planning and design )CPD), transportation and connectivity (TRC), building and resources (BDR) and business and innovation (BSI) (Greenbuildingindex, 2012a)
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W/m2) and less than 20 Watts per meter square (RTTV ≤ 25 W/m2, respectively (Greenbuildingindex, 2011).
The Green Building Index (GBI) Malaysia was launched in 2009 by Architects Association, Malaysia (Pertubuhan Arkiteck Malaysia - PAM) in collaboration with the Association of Consulting Engineers Malaysia (ACEM). Greenbuildingindex Sdn Bhd was created as a wholly-owned company that is subsidized by PAM and ACEM for administration and training of GBI accreditation, facilitators and certifiers (Greenbuildingindex, 2012b). Support for the GBI in the building industry will help building designers creates buildings to fit local climatic context, for example by reducing its energy consumption and minimizing its environmental impact. However, based on current GBI assessment criteria, GHG emissions associated with energy use in building operation are not evaluated. Without strategies to measure GHG emissions from building operations, the Malaysian building sector loses an opportunity in its effort reduce its environmental impact.
The GBI aims to define and establish a common definition of green building in the Malaysian context, and to provide a standard method of measurement (Greenbuildingindex, 2012b). The GBI measures the ‘greenness’ of buildings based on six (6) criteria, namely energy efficiency, indoor environmental quality, sustainability in site planning and management, materials and resources, water efficiency and innovation (Greenbuildingindex, 2012b). Green buildings can be defined as buildings that “incorporate design techniques, materials and technologies that minimize its overall impacts on the environment and human health” (Mohanty, 2012, p. 13).
Green buildings most often feature strategies like natural ventilation capabilities (Deuble & de Dear, 2012), reduced resource consumption and waste production and are aimed at creating a healthy indoor environment (Wang et al., 2005). Green buildings are also designed to minimize their life-cycle environmental impacts (Graham & Booth, 2010) and cost (Wang et al., 2005). The green building concept can also be used to not only to improve local but also regional and global ecosystems (Burnett, 2007). This clearly indicates that the green building concept is multifaceted and can be defined or interpreted in many ways to meet local needs, as noted by Kubba (2012).
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The GBI green rating tool that mainly caters for new construction and private residential properties, with a minimum registration fee of RM5,000 (approximately US$ 1,507) for a single residential unit less than 2,000 m² (Greenbuildingindex, 2009). The GBI currently is also not applicable to existing low-cost housing typology, and as low- cost housing typology is mandatory for almost all housing development projects, there is a large opportunity for policy inclusion and improvement.
Therefore, while there has been some significant research in Malaysia on rating schemes, this does not adequately inform policy for low-cost housing. A different framework for assessing the operational energy performance for low-cost housing is needed to address the deficiencies in the existing GBI tool and the absence of an energy efficient code for the residential sector. Additionally, as Malaysia still lacks a consistent GHG emissions database (Fong et al., 2008), this large gap in knowledge needs to be addressed if Malaysia is to achieve its 40% GHG reduction by year 2020. Addressing this need, the assessment framework designed for this research is described in Chapter 4 on research design and methodology.
2.3 Implementing Energy Efficiency and Energy Performance Building Codes
An increase in building energy-related GHG emissions in the past decade has also come with improvements in building policies. Mitigation strategies in buildings are not only environmentally beneficial, they are also associated with associated co-benefits, such as social welfare for low-income households; increased access to energy services, improved indoor air quality, comfort, quality of life and health; job creation; and economic competitiveness (IPCC, 2007d).
Building stock energy consumption models are key in implementation of mitigation policies (Kavgic et al., 2010), as policy makers are able to review policy impacts at end- use level. Ongoing development of such policies in the building sector should be periodically reviewed in order to monitor their effectiveness, if not their overall implementation rate on the building stock. Kavgic, et al. (2010) argues that building stock models for energy consumption should acquire the ability to:
1) Estimate the ‘baseline’ energy consumption of the residential housing sector, disaggregated by building or social categories and energy end-uses;
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2) Explore the technical and economic effects of different CO2 emissions35 reduction strategies over time, including the impact of new renewable technologies and smart metering; and 3) Not be confined to issues directly related to energy, but identify the effect of emissions reduction strategies on indoor environmental quality (Kavgic et al., 2010 p.1684).
As mentioned in Chapter 1, the building sector has been identified by the Intergovernmental Panel on Climate Change (IPCC) as the single largest sector for GHG mitigation potential in all countries (see Figure 2.1) (Barker et al., 2007b; IPCC, 2007a; UNEP-SBCI, 2010b). GHG mitigation potential can be defined as “the level of GHG emission reductions that could be realized, relative to the projected emission baselines in a given year, for a given carbon price” (OECD & IEA, 2009 p.6). In order to develop GHG mitigation and energy modelling policies, baseline data are needed (Strachan, 2011).
Energy and GHG emissions baselines are mostly readily available at the national level for most developed economies, but are uncommon at the local or sectoral levels (Hamilton et al., 2008; Huovila et al., 2009). Local or sectoral baseline profiles can be generated through a top-down and bottom-up configuration that allows for very specific and localized aggregation. Numerous GHG mitigation models have been created internationally to project and estimate future GHG mitigation potential, such as G- Cubed, GTEM, MMRF (from Australia); EC_IDYGE, E3MC (from Canada); ADAGE, EPPA, SGM (from the USA); GEM-E3, POLES (from the EU); ENV-Linkages, GAINS, McKinsey, WITCH (from international institutions, corporations and inter- governmental organizations) (OECD & IEA, 2009; Strachan, 2011). Such GHG mitigation models are an estimation of real world conditions and the different baseline assumptions for each model leads to substantial differences in the projected mitigation potential (OECD & IEA, 2009).
35 CO2 emissions in this context means carbon dioxide emissions as per the European Union (EU) Directive on the Energy Performance of Buildings (Directive 2002/91/EC) (Kavgic et al., 2010).
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Figure 2.1 Economic mitigation potential by sector in 2030 estimated from bottom-up studies Source: IPCC (2007a)
A recent Global Building Performance Network (GBPN) report compared 18 global and regional energy saving and GHG mitigation potential studies, including those from the International Energy Agency (IEA), World Business Council on Sustainable Development (WBCSD) and McKinsey (refer Figure 2.2) (Urge-Vorsatz et al., 2012a). Notwithstanding the different assumptions, methods and projection periods of the compared models, similar trends emerged, that is building energy consumption is projected to grow extensively in the next few decades (Urge-Vorsatz et al., 2012a). Final building energy consumption and its corresponding GHG emissions is estimated to grow between 60% to 90% of the 2005 values by 2050 using BAU projections (Urge-Vorsatz et al., 2012a). The report also iterated that improving energy efficiency in the building sector is not enough to bring significant emissions reduction (Urge-Vorsatz et al., 2012a).
Most importantly, the report pointed out that reducing energy use for heating and/or cooling holds the largest mitigation opportunity as compared to other building end-use (Urge-Vorsatz et al., 2012a). Therefore performance-based building policies are needed to unlock this heating/cooling efficiency potential (Urge-Vorsatz et al., 2012a). This presents a good starting point for Malaysian policy makers in developing holistic energy efficient and/or performance strategies in the building sector, focusing on energy reduction for cooling needs.
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Figure 2.2 Global Final Energy Demand Projections by Various Models (between 2005 to 2050) Source: Urge-Vorsatz, et al. (2012a)
Under the Kyoto Protocol, there are three market-based mechanisms in place to reduce developed countries GHG emissions, i.e. the Clean Development Mechanism (CDM), Joint Implementation (JI) and Emissions Trading (UNFCCC, 2013a). The CDM is used to trade certified emissions reduction (CER) credits from emissions-reduction projects, between developed countries and developing countries (UNEP, 2009; UNFCCC, 2013a). However, the effectiveness in implementing CDM in the building sector has been lower than expected despite the high potential for emissions reduction from the sector (Koeppel & Urge-Vorstaz, 2007; UNEP, 2009). This is likely due to the lack of baselines in many developing countries, and a lack of established or enforced energy efficiency standards that could be used as an alternative benchmark (UNEP, 2009).
The Joint Implementation mechanism allows developed countries with obligations (Annex 1 Countries) under the Kyoto Protocol to transfer and/or acquire emissions reduction units (ERU), between the two developed countries (UNFCCC, 2013a; World Bank, 2011). Another emissions reduction scheme called Nationally Approved Mitigation Action (NAMA) was developed as a voluntary approach to encourage developing countries reduce their GHG emissions by technological and capacity assistance from developed countries (UNEP-SBCI, 2010b). The building sector holds high prospect for NAMA projects to be implemented in developing countries (UNEP, 2009), and should be encouraged by more energy efficiency building codes or standards.
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Significantly, more half of the world’s new construction is occurring in Asia (Levine et al., 2007). For example, India’s built-up area has more than doubled within the last 10 years (Hong et al., 2007). China on the other hand represents almost half of the world’s new construction, and its energy demand for space heating and hot water accounted for 40% of the world total (Hong et al., 2007; Levine et al., 2007). A recent initiative by GBPN brought to light China’s building energy performance and building energy policies that were previously unavailable in English. The report drew on Chinese publications that presented analysis of the effectiveness of China’s building energy codes and policies (Bin & Jun, 2012). Such analysis and insights can be exemplary to other developing countries (albeit in a different context and climate).
China’s energy efficiency building standards were formulated in stages, beginning with an energy design standard for residential buildings in 1986 and leading to a national energy efficient standard in 2007 (Huang & Deringer, 2007). This can be further broken into four development stages (Bin & Jun, 2012 p.9-10):
1. Research development (early 1980s to 1986) 2. Pilot and demonstration projects (1987 to 1993) 3. System formulation of regulatory, administrative and technical support (1994 to 2005) 4. System improvement and policy implementation (2006 to present)
China’s building energy codes require all new buildings to comply with energy efficiency requirements for its Acceptance Codes36 that are supervised and inspected by a third party. The Chinese government have also provided financial support for a series of policies for existing buildings, in promoting heat reform37 and retrofitting. Additionally, large scale energy efficiency improvement initiatives were implemented in its public buildings (including government office buildings and university buildings). These buildings were then audited to develop energy consumption data and disseminate it publically (Bin & Jun, 2012).
36 “Acceptance Codes makes compliance with building energy efficiency requirements mandatory for the final acceptance of a construction project” (Bin & Jun, 2012 p.11). 37 Heat reform policy aim is to “reduce the amount of energy wasted by end users through the reform of the heating pricing system and by establishing market mechanisms and to stimulate heat suppliers’ efforts to improve the energy efficiency of their heat supply networks” (Bin & Jun, 2012 p.12).
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United Nations Environment Programme through the Sustainable Buildings and Climate Initiative (UNEP-SBCI) recommends five policy objectives for reducing emissions from buildings, and the main policy objective should be to “increase the energy efficiency of new and existing buildings (both the physical envelope, and the operational aspects such as energy systems for heating, ventilation and other appliances” (Huovila et al., 2009 p.23). Policies in reducing GHG emissions from the building sector can be categorized as 1) regulatory and control instruments; 2) economic and market-based; 3) fiscal and incentives; and 4) support, information and voluntary actions (Huovila et al., 2009; Levine et al., 2007).
Industries play a significant part in promoting market-led energy efficiency transformations, as in North America and Europe (Hong et al., 2007). The situation in Malaysia seems to lean towards an industry-led rather than regulatory-led transformation in absence of energy efficiency legislation within the building sector. The current Uniform Building By-Laws (UBBL) have not included any energy efficiency considerations (UNEP & BCA, 2011). Current initiatives such as the building rating tool (Green Building Index) are industry led, developed by the Association of Malaysia Architects and Association of Consulting Engineers (UNEP & BCA, 2011).
The Malaysian Green Building Confederation (MGBC) promotes sustainable building initiatives by conducting courses and workshops to create greener building, is also a non-governmental and non-profit organization (MGBC, 2011; UNEP & BCA, 2011). MGBC also created ‘Green Pages Malaysia’, the first green products and services directory in Malaysia (MGBC, 2011; UNEP & BCA, 2011). Other awareness programmes such as ‘SWITCH’ to promotes energy efficiency for domestic, commercial and industrial energy consumers was also conducted by the Water and Energy Consumer Association of Malaysia (WECAM), which is also a non-governmental organization (UNEP & BCA, 2011).
2.3.1 Energy Performance Standards in the Building Sector
Generally, there are two major types of building energy standard, 1) prescriptive and 2) performance-based standards (Iwaro & Mwasha, 2010). Prescriptive standards sets multiple and disaggregated performance levels for major building components, such as minimum thermal resistance in walls (Iwaro & Mwasha, 2010). Meanwhile
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performance-based standards sets overall energy consumption level of the building or its energy cost budget (Huovila et al., 2007; Iwaro & Mwasha, 2010).
Prescriptive standards are more widely used than performance-based standards, most likely due to easier implementation and enforcements of designated building parts (Iwaro & Mwasha, 2010). Nation-wide building standards are generally prescriptive in regulating the physical, thermal and electrical requirements of building components, service systems and equipment to help promote energy efficiency (Iwaro & Mwasha, 2010; Lee & Chen, 2008). However performance-based standards allow flexibility and can provide incentive for innovation (Iwaro & Mwasha, 2010).
Prescriptive energy efficient options for building operation are, for example, high- efficiency lighting and appliances, efficient ventilation and cooling systems, and solar water heaters (Levine et al., 2007). However, the pattern of energy consumption is strongly dependant on building type and climate zone, which is why there is no universal solution available for improving the energy efficiency of buildings (Huovila et al., 2007). Therefore, building codes and standards must adhere to local conditions, especially for space heating and cooling functions (Huovila et al., 2007).
Accessible and cost-effective strategies include passive solar design, high-reflectivity building materials and glazing, and effective thermal envelopes and insulation materials (Levine et al., 2007). Refurbishing the thermal properties of existing building envelopes was found to be one of the most logical solutions in reducing a building’s energy consumption (Huovila et al., 2007; Kolokotsa et al., 2011). Upgrading roof fittings and cavity wall insulation was found to provide investment returns in all European countries, and significant benefits for warm and moderate climates, due to the currently low standards of insulation and increased demand for air-conditioning (Huovila et al., 2007).
Buildings within the tropical climate should pay particular attention to passive comfort measures such as natural ventilation, night-time ventilation, solar control or shading, and lightweight construction (Huovila et al., 2007). It was also found that reducing cooling loads often posed a higher challenge than reducing heating loads (Huovila et al., 2007). Design principles to reduce cooling loads are (Huovila et al., 2007; Levine et al., 2007):
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i) Minimizing the wall area facing east or west; ii) Clustered building schemes for self shading; iii) High-reflective building materials; iv) Air-tight structures; v) Ventilation heat recovery systems; vi) Insulation and thermal walls in minimizing daytime indoor temperature; vii) Fixed or adjustable shading; and viii) Glazing on windows, particularly on east and west facing walls
Energy for cooling loads of buildings is highly dependent on the building shape, orientation and building materials. These design principles could be integrated in building codes and standards to help reduce operational energy consumption. The following section explores the concept of low energy or zero energy buildings as performance based standards in buildings.
2.3.2 Low and Zero Energy Performance Targets for Buildings
Energy performance building codes limits the amount of energy consumption according to building type (Huovila et al., 2007). Example of energy performance building codes are the French Low Energy Building Decree - Batiment Basse Consommation (BBC) that sets an energy requirement of 50 kWh/m2/year for new housing, the Norwegian Standard for Residential Passive House (NS 3700) and the European Union Directive on Energy Performance of Buildings (EPBD 2002/91/EC), which requires new buildings to be nearly zero energy buildings by the end of 2020 (Economidou et al., 2011; Sartori et al., 2012; Thiers & Peuportier, 2012).
The definition of a low-energy building can be categorized into two specific concepts, i.e. the 50% energy reduction, or 0% energy (zero-energy) in comparison with energy consumed in a standard building constructed according to building regulations (Huovila et al., 2007). However in recent years, zero-energy buildings (ZEBs), nearly zero-energy buildings (nZEB) and/or positive-energy buildings (PEBs) have become increasingly a priority for researchers and policy experts (BPIE, 2012; European Parliament, 2010; Kolokotsa et al., 2011).
ZEBs have also become part of energy policy in the European Union’s EPBD 2002/91/EC (Sartori et al., 2012; Thiers & Peuportier, 2012). Zero-energy buildings can be defined
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as “buildings that produce as much energy as they consume over a full year” (Huovila et al., 2007 p.26). ZEBs and PEBs are a progression of the design concept from passive and/or sustainable design (Kolokotsa et al., 2011). This concept requires state-of-the-art energy efficient technologies and renewable energy systems, usually on-site, to provide energy sources equal to the energy consumed by the building (Huovila et al., 2007).
Most zero-energy buildings produce energy and are connected to electricity grids as a mechanism to cope with possible fluctuations in energy demand (Huovila et al., 2007; Kolokotsa et al., 2011). These buildings are usually referred to as ‘net zero-buildings’, where there is exchange between energy consumed and exported back to the grid, usually calculated within a year (Sartori et al., 2012). However, ZEBs are more commonly understood as energy efficient buildings that can also generate energy (usually electricity) from renewable resources, that may or may not be connected to the electricity grid (autonomous buildings) (Sartori et al., 2012). Autonomous buildings are buildings that operate independently from energy service infrastructures, or the electricity grid (Sartori et al., 2012). Consequently, ZEBs can be used as an alternative to economic vulnerabilities, such as the high dependency on fossil fuels and fuel imports (Huovila et al., 2007).
Meanwhile, nearly nZEB is a “building that has a very high energy performance, as determined in accordance with Annex I. The nearly zero or very low amount of energy required should be covered to a very significant extent by energy from renewable sources, including energy from renewable sources produced on-site or nearby” (European Parliament, 2010, p. 18). Nearly-ZEB was introduced in 2010 to the EU Directive 2010/31/EU, which requires new public buildings to be nearly zero-energy by 2019, and all new buildings to be nearly zero-energy by 2021 (BPIE, 2012; European Parliament, 2010).
As PEB is a fairly new design concept, a common definition is still in its discourse as several alternatives remain probable (Thiers & Peuportier, 2012). One difference between ZEBs and PEBs lies in the amount of energy produced. ZEBs produce the same amount of energy consumed, and PEBs produce more energy than they consume annually (Bojić et al., 2011; Thiers & Peuportier, 2012). It has also been argued that the energy balance should be calculated in a life-cycle approach, accounting for embodied
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energy involved during construction, retrofit/renovation and demolition phases for a more comprehensive assessment (Thiers & Peuportier, 2012).
Design improvements for energy efficiency that have been associated with nZEBs, ZEBs and PEBs include the use of renewable sources like photovoltaic, intelligent energy management devices like sensors and monitoring systems, and high efficiency heating and cooling equipment like solar air-conditioning (Kolokotsa et al., 2011). ZEBs and PEBs should be designed to an extent, to work in synergy with electricity grids, tailoring to their localized energy infrastructure and climate (Sartori et al., 2012). As buildings function as a system, it would be more efficient to implement whole building concepts than to develop specific technologies, since low-energy buildings have higher reliance on construction practices and user behaviour than conventional buildings (Huovila et al., 2007).
However, nZEBs, ZEBs and/or PEBs do not necessarily guarantee reduction of their energy-related environmental impacts (Sartori et al., 2012). Unpredictable user behaviour during building operation could adversely affect energy efficiency, such as setting temperature too high/too low, and unnecessary usage of lighting or air- conditioning systems (Kolokotsa et al., 2011). Window use can also effect energy efficiency and thermal comfort so that opening windows during hot summer days and closing them at night increases building cooling load (Kolokotsa et al., 2011).
Innovative user interface that facilitates communication building operation and energy consumption enhances the user’s awareness and helps present rational and more efficient use of energy (Karjalainen & Lappalainen, 2011; Kolokotsa et al., 2011). Such user interface could provide relevant information to indoor comfort and energy conservation awareness, such as daily temperature, weather forecast and energy prices (Karjalainen & Lappalainen, 2011).
In the absence of good empirical data on building performance, modelling and simulation have become an ‘a priori’ approach for designers (Kolokotsa et al., 2011). However, as simulation models produce predicted outcome, designers should not mistake the results of a simulation to that of an MRV approach where baselines are projected based on empirical and actual building performance data.
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With nZEBs and ZEBs being implemented as mandatory requirement for building performance in the European Union, consequential research should investigate effectiveness of such mandatory building performance requirements in reducing overall energy consumption and GHG emissions from building operation. The following section discusses the advantages and disadvantages of both mandatory and voluntary energy efficiency measures.
2.3.3 Mandatory and Voluntary Energy Efficiency Measures
The absence of a market for GHG reduction limits motivation and investment in climate-friendly technologies (Brown et al., 2007). More stringent environmental policies to push for low-carbon and/or renewable fuels, together with energy efficiency is needed for the building sector (Urge-Vorsatz et al., 2012a). Enforcing energy performance requirements in building codes and enforcing energy efficiency obligation is the most cost and technically effective strategy in reducing GHG emissions from both existing and new buildings (Huovila et al., 2009; Urge-Vorsatz et al., 2012a).
Energy performance or energy efficiency building codes (EEBC) are often used to implement energy efficiency standards, such as energy consumption of entire buildings or building systems (Birner & Martinot, 2002; Koeppel & Urge-Vorstaz, 2007). Energy efficiency and renewable energy utilization in the building sector offers an extensive “portfolio of options where synergies between sustainable development and GHG abatement exist” (IPCC, 2007d p.390). In other words, enforcement of energy efficient building standards through regulatory control can potentially ensure a minimum level of performance is achieved across the building sector (Huovila et al., 2007; Huovila et al., 2009; Lee & Yik, 2004).
Mandatory EEBC also provides an incentive for the private sector to invest in new technologies (Huovila et al., 2009). Additionally, in order to stay effective, building codes need to be regularly revised as technology advances and costs less (Birner & Martinot, 2002). This would lead to greater efficiency, with the removal of the least efficient products and the purchase of more efficient products in the market place (Birner & Martinot, 2002).
However, as the building sector is a complex field that is highly interconnected, it faces unique challenges and shortcomings in implementing energy efficiency regulations
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(Peterman et al., 2012). Identified shortcomings for mandatory EEBC are such as lack of awareness and energy pricing that does not fully reflect true environmental costs of energy consumption (Peterman et al., 2012). Additionally, compliance to mandatory EEBC are not driving corporations or private stakeholders to address sustainability challenges (Peterman et al., 2012).
Furthermore, effectiveness of building codes and standards are highly dependent on the level of enforcement and implementation (Birner & Martinot, 2002; Deringer et al., 2004; Iwaro & Mwasha, 2010). EEBCs in developing countries mostly exist on paper and have failed to significantly impact energy savings or reductions, which is largely due to lack of enforcement and implementation, lack of industrial awareness and financial capacity, and corruption (Deringer et al., 2004; Koeppel & Urge-Vorstaz, 2007; UNESCAP, 2010). Lack of competent enforcement agencies and conflicting code requirements between local, state and federal are also barriers to implementing EEBCs (Huang & Deringer, 2007; Lee & Yik, 2004).
Most EEBCs also tend to be applied for new construction projects, which excludes the vast existing buildings portfolio (Peterman et al., 2012). Despite the residential sector’s large contribution to building energy-related emissions, most energy performance building codes in developing Asian countries are mandatory only for commercial or non- residential buildings (Janda, 2009). This presents a large potential for efficiency improvements and GHG savings that has not been addressed by regulations. Residential buildings are neither as well regulated nor are they promoted as a target for energy efficiency (APEC, 2011b). Both aspects are likely to have significant implications for the energy end-use performance of the residential building sector (APEC, 2011b).
South East Asian (SEA) countries like Cambodia, Lao People’s Democratic Republic (PDR), Malaysia and Myanmar have only voluntary energy performance building codes for non-residential buildings (Janda, 2009; Komori et al., 2012). Singapore, Indonesia and Thailand are examples of SEA countries that have introduced mandatory energy performance building standards for both residential and commercial buildings (Komori et al., 2012). A brief EEBC introduction for SEA is presented in the following section.
Lee & Yik (Lee & Yik, 2002, 2004) suggest a voluntary-based environmental approach is more effective, because it offers greater flexibility for stakeholders to achieve their
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target. Voluntary strategies such as energy audits, rating and awards programmes generates more awareness for building owners and users alike to improve the building’s energy performance (Managan et al., 2012). However, voluntary energy audits only provides information on building energy consumption, therefore without any action this policy tool is ineffective and must to be paired with complementary building codes and/or subsidies for energy efficiency upgrades (Managan et al., 2012).
Financial incentives in the form of tax deductions are often used encourage building owners or tenants to implement energy efficiency strategies in building operation and retrofits (Economidou et al., 2011; Managan et al., 2012). Voluntary rating and certification programmes are mechanisms to reward top performers in achieving high efficiency rating, through building rating tools such as LEED, BREEAM and Green Star (Managan et al., 2012). Evidence from various countries in promoting rating systems has helped develop a body of knowledge to promote sustainability and in mitigating the building sector’s environmental impact (Hong et al., 2007; Huovila et al., 2007).
Public competitions and award programmes such as United State’s ‘Battle of the Buildings’ that involved 245 buildings throughout the nation, aimed at outperforming each other in terms of energy savings (Managan et al., 2012). Economic incentives coupled with voluntary programmes will also enable a more effective implementation, in efforts to address inherent barriers to regulatory frameworks (Lee & Yik, 2002, 2004; Peterman et al., 2012).
Nonetheless, significant transformation in energy consumption and emissions reduction is not possible without mandatory building codes and ambitious national policies (Urge- Vorsatz et al., 2012a). For example national building codes in Scandinavian countries are used to regulate the physical and operational service systems and equipment used in buildings to standardize energy efficiency (Koeppel & Urge-Vorstaz, 2007; UNEP, 2009). Experience in public sector buildings with mandatory energy reduction programmes in effect, have also shown significant energy savings. Federal agencies in the U.S. were obliged to reduce their energy consumption by 35% by 2010 compared to 1990 levels (U.S. DOE, 2006 cited in 2009). This led to energy savings of 4.8 GWh annually (and 2.3 ktCO2e/year) and cost savings of USD$ 5.2 billion (U.S. DOE, 2006 cited inHuovila et al., 2009).
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2.3.4 Energy Efficiency Legislation and Policies in South East Asia
The United Nations “Assessment report on energy efficiency institutional arrangements in Asia” (UNESCAP, 2010), suggests that countries in South East Asia (SEA) share common barriers in promoting energy efficiency, i.e. lack of industrial awareness, lack of financial capacities, and lack of confidence in technology (UNESCAP, 2010). Brunei is in the midst of introducing its own Energy Efficient Building Guidelines that are expected to become mandatory and supplement the existing National Building Code (APEC, 2011a; UNEP & BCA, 2011). The guideline is set to cover building envelope, cooling and ventilation, lighting, heating, insulation, site orientation, and building design (UNEP & BCA, 2011). Notwithstanding the EEBGs, there have been voluntary initiatives taken by the government and industry in the energy efficiency area.
Cambodia has been promoting energy efficiency through programmes such as the Promotion of Energy Efficiency and Conservation (PROMEEC) with the Energy Conservation Centre of Japan (ECCJ), and capacity building with the French Agency for the Environment and Energy Management (UNEP & BCA, 2011). Cambodia also participates in the ASEAN Energy Awards, which promote regional cooperation and partnerships between the private and public sectors (UNEP & BCA, 2011) .
Indonesia applies mandatory energy conservation best practice measures for all its government office buildings. Government offices are expected to submit monthly energy consumption reports every six months (APEC, 2011a; UNEP & BCA, 2011). Another mandatory framework (Law No.28/2002 regarding Buildings), requires all buildings to comply with existing energy standards that apply to building envelopes, air- conditioning, lighting, and energy auditing (APEC, 2011a; UNEP & BCA, 2011).
The Philippines introduced the Guidelines for Energy Conservation Design of Buildings and Utility Systems into its National Building Code (Republic Act No. 6541) (OCEAN, 2009; UNEP & BCA, 2011). However, the energy efficiency section of this building code is presented on a voluntary basis, and only concerns building envelope, lighting, HVAC (heating, ventilation, and air conditioning), and water heating (OCEAN, 2009). A mandatory Energy Efficiency Labelling scheme was also introduced to label energy efficient refrigerators, window-type air-conditioners, compact fluorescent lamps and linear fluorescent lamps, targeted at all types of building (UNEP & BCA, 2011).
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Thailand’s Building Energy Code was introduced in 1994 and enforced in 1995, which covers building envelope, HVAC and lighting requirements (OCEAN, 2009). The mandatory code is applicable to all new and existing commercial and government buildings which must comply with the maximum standard of 55 watts per square meter (m2) of gross floor area (g.f.a) (OCEAN, 2009). Other regulatory instruments have been implemented, like the Minimum Energy Performance Standards (MEPS), the Royal Decree on Designated Buildings/Factories, and the mandatory energy efficient refrigerator/air-conditioner programme (UNEP & BCA, 2011).
Vietnam introduced its Energy Efficiency Building Code (No. 40/2005/QD-BXD) and then the Energy Commercial Building Code (No. 40/2005/QB-BXD) in 2005 (OCEAN, 2009; Pham, 2011). These are applicable to public, residential and non-residential buildings, and cover building envelope, lighting, air-conditioning and ventilation (APEC, 2011a; UNEP & BCA, 2011).
Vietnam also have implemented an Energy Efficiency Standards and Labels regulation that prescribes energy performances of appliances, equipment and lighting (UNEP & BCA, 2011). There are three types of standards, namely the minimum energy performance standards (MEPS), prescriptive standards and the class-average standards (UNEP & BCA, 2011). The MEPS prescribe the minimum efficiency level or the maximum allowable energy consumption for applicable products, while the prescriptive standard requires “that a particular feature or device to be installed in all models of new products indicated” (UNEP & BCA, 2011, p. 29). The class-average standard specifies the average efficiency level required “across all models of a manufactured product, allowing manufacturers to select each model’s level of efficiency such that the overall prescribed average is achieved” (UNEP & BCA, 2011, p. 29).
Singapore’s extensive sustainable building and energy efficiency commitment is reflected in its various initiatives that range from regulatory legislation, fiscal instruments, market-driven initiatives and voluntary schemes. Singapore’s Building and Construction Authority (BCA) introduced its Building Control (Environmental Sustainability) Regulations in 2008 (BCA, 2008; UNESCAP, 2010). This legislation requires all new buildings (residential and non-residential) and additions/extensions to existing buildings, with a gross floor area of 2,000 square metres or more, to comply with a minimum Green Mark Score of 50 points (BCA, 2008; UNESCAP, 2010). The
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Green Mark assesses buildings by energy efficiency (minimum 30 points), and other green requirements (minimum 20 points) such as water efficiency, environmental protection, indoor environmental quality and other green features (BCA, 2012a).
This standard is divided into two general categories, i.e. energy related requirements and other green requirements (BCA, 2012b). Looking at the residential building criteria for energy related requirement, it specifies the minimum permissible envelope transmittance value (RETV) for thermal performance of 25 W/m2 (BCA, 2012b). This specification is aimed to “enhance the overall thermal performance of building envelope to minimise heat gain thus reducing the overall cooling load when required” (BCA, 2012b, p. 9).
Also included in this standard is specification for natural ventilation design and air- conditioning systems (BCA, 2012b). This section is expected to “enhance dwelling unit indoor comfort through the provision of good natural ventilation design and energy efficient air-conditioners” (BCA, 2012b, p. 9). Requirement for natural air flow within dwelling units are achieved through building layout design that “utilizes prevailing wind conditions to achieve adequate cross ventilation” and through individual dwelling unit that is designed to provide sufficient openings (BCA, 2012b, p. 9). Points are calculated for dwelling units “with window openings facing north and south directions” and for “living rooms and bedrooms designed with true cross ventilation” (BCA, 2012b, p. 9). In the provision of air-conditioning systems, it requires air-conditioning systems “that are certified under the Singapore Energy Labelling Scheme” (BCA, 2012b, p. 9).
Other relevant building codes implemented in Singapore are the Code on Envelope Thermal Performance for Buildings; the Code of Practice for Energy Efficiency Standard for Building Services and Equipment, and the Code of Practice for Air-Conditioning and Mechanical Ventilation in Buildings (UNEP & BCA, 2011; UNESCAP, 2010). A brief comparison of existing EEBCs in SEA is summarized in Table 2.2. Notwithstanding the policies and statues in place, the more pressing issue is their effectiveness in implementation, which requires further investigation that falls beyond the scope of this research.
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Energy Efficiency Strategies and Building Codes in South East Asia Country Mandatory Voluntary
Brunei None. Energy audits; EE Building Guidelines; EE and Darussalam Conservation Initiative Awards Scheme; EE Labelling Scheme.
Cambodia None. Energy Audits in Commercial Buildings; Promotion of EE and Conservation (PROMEEC).
Indonesia Building Codes - Energy Provision; Mandatory EE Labelling Systems; GREENSHIP Building Rating Tool; energy conservation for government buildings; Public-Private Partnership Programme on Energy Energy Building Standards- Indonesian National Conservation; Energy Conservation Clearinghouse; Standard; Minimum Energy Performance Energy Benchmark and Best Practice Guide (for Standards and Labelling; Presidential Instruction commercial buildings). No. 10/2005 on EE.
Malaysia None. Energy Rating and Labelling Programme; Malaysian Standard-Code of Practice on the Use of Renewable Energy and EE in Non-Residential Buildings (MS1525:2007); Green Building Index (GBI); Energy Performance Management Systems (EPMS) – energy audits on government buildings
Myanmar None. None.
Philippines Building Codes-Guidelines for Energy Building for Ecologically Responsive Design Excellence Conservation Design of Buildings and Utility (BERDE) Building Rating System; Green Building Systems; Mandatory EE Labelling; Government Initiative (GBI) Rating System; EE Standard and Energy Management Programme (GEMP) – Labelling Programme; Energy Audits by Dept. Of energy audits; Malacanang Administrative Order Energy under National EE Conservative Programme (OA) No. 103, 183, 228. (NEECP); GEMP Award.
Singapore Building Control (Environmental Sustainability) Green Mark Schemes; Singapore Green Building Regulations; Code on Envelope Thermal Product Certification Scheme; Eco-Office Label; Performance for Buildings; Code of Practice for Singapore Green Labelling Scheme (SGLS) ; EE Building EE Standard for Building Services and Equipment; Benchmarking Programme; online benchmarking Code of Practice for Air-Conditioning and system (Energy Smart Tool); Built Environment Mechanical Ventilation in Buildings; Mandatory Leadership Award; Green Mark Champion Award. Energy Labelling Scheme (MELS).
Thailand Building Energy Code, Minimum Energy Green Leaf Programme, Thai Green Label Scheme; EE Performance Standards (MEPS); Designated Labelling (No.5); High Energy Performance Standards Buildings/Factories under Energy Conservation (HEPS); EE Building Labelling Scheme; Government Promotion Act; Mandatory Audits under Building Buildings Audit and Retrofit Programme; Private Energy Code (for designated buildings). Building Energy Audits.
Viet Nam EE Building Code; EE Standards and Labels; EE LOTUS VN Green Rating Tool; ASEAN Energy Commercial Building Code; Public Procurement Management Scheme; Promotion on EE and of Works and Constructions – Law on Conservation (PROMEEC). Construction (No.16/2003/QH11); Law on Environmental Protection (No. 52/2005/QH11).
Table 2.2 Energy Efficiency Strategies and Building Codes for South East Asian Countries Source: (APERC, 2011; OCEAN, 2009; UNEP & BCA, 2011; UNESCAP, 2010)
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It seems that most South East Asian countries are taking positive steps in implementing energy efficiency and energy performance building codes, and have already in place mandatory building codes, either for residential or non-residential sectors, or both. Most importantly, all of the SEA countries have introduced voluntary energy efficiency and energy performance building codes, which reflect the seriousness of national views about the matter. Malaysia is presented an opportunity to develop its own sets of policies by studying the wide variety of policies and EEBC available from neighbouring SEA countries, as it is region specific and experiences similar climatic conditions.
2.4 The Need for Baselines to Inform Policy Development
A baseline study is an analysis of the current situation and is used to identify starting points, against which a program of improvements can be assessed (European Commission, 2010). Baselines are essential for reporting and monitoring emission reductions. They also enable stakeholders to look at actual improvements as well as the opportunities that might exist (UNEP-SBCI, 2010). Research by Gustavsson, et al., (2000) in GHG accounting adopted a general definition of baselines as “a path through time that an accounting variable would have followed in the absence of a specific greenhouse-gas mitigation activity” (Gustavsson et al., 2000, p. 936). In other words, a baseline is the projection of GHG emissions that would occur in the absence of any mitigation action (Lee et al., 2005). Baselines are also known as reference case or business-as-usual (BAU) accounting (IEA, 2009; Strachan, 2011).
The implementation of baselines for energy and GHG emissions has been generally accepted as a key input assumption for long-term energy modelling (Strachan, 2011). Energy consumption and associated GHG emission baselines would generally require two elements of primary information: the average energy intensity (energy use per square metre) and the emissions factor for electricity produced (Ellis et al., 2001; Lee et al., 2005; UNEP-SBCI, 2010b).
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However, it must also be noted that baselines are subject to uncertainties (Ellis & Bosi, 2000; Lee et al., 2005). Miscalculations in projecting baselines could trigger increased emissions, where overtly high baseline predictions, with erroneous credits38, would enable increased emissions without actual reductions being made (Kartha et al., 2004). Conversely, stringent baselines with low reduction credits could result in the loss of investment opportunities, or claim higher costs for emissions target compliance (Kartha et al., 2004).
Overestimation of GHG credits in artificially high baselines can be manipulated to inflate a project’s impact to reduce net emissions, and therefore increases the credit for that activity Credits for emission reductions should not exceed the improvements in a global system’s accounting, and should be less than or equal to the mitigation benefits that actually occur. Large artificial reductions used as financial manipulation only to benefit the seller and buyer. The former’s projects become more profitable and the latter’s emission reduction costs are inexpensively fulfilled (Gustavsson et al., 2000).
Basic principles such as accuracy, transparency and conservatism helps to develop more scientifically sound and technical useful baselines (Gustavsson et al., 2000; Lee et al., 2005). These principles are paramount if uncertainties are to be overcome, as emissions projection models are only an approximation of reality and with further projections into the future, the greater the uncertainty (OECD & IEA, 2009). Realistic long-term projections or BAU baseline models should also consider existing policies in place for energy efficiency, technological advancements, and exclusion of market barriers (Strachan, 2011).
Accurate description of the net emissions path, in the absence of any policy intervention, should be the main purpose of a GHG baseline; while capturing the consequences of all alternatives in mitigation activities, for comprehensiveness (Gustavsson et al., 2000). Nevertheless such policies are not expected to remain stagnant, as governments change and alternative approaches are revised (Strachan, 2011). Moreover as impacts of these policy remain to be in the future, its deliverance of objectives remains vague in its quantified energy and/or emissions reduction (Strachan, 2011).
38 “Note that this concern exists largely for the CDM. In the context of JI, where host countries have a fixed emission target, overly high baselines will simply reduce the host country’s access to emissions rights cover domestic emissions and to sell through emissions trading” (Kartha et al., 2004 p.546).
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Therefore, long-term climate mitigation policies must ensure transparency in their model baselines, and especially critical for those that have already been implemented for a number of years (Strachan, 2011). This is to avoid under/overestimation of true costs of long-term emissions reduction policies, and to perform cost-effectively (Strachan, 2011). Transparency is also important in ensuring the baselines established are without bias and replicable by any third party (Lee et al., 2005). This conversely aids the establishment of a more comprehensive and accurate baseline.
A number of assumptions are likely to be made while establishing the key parameters in developing the baseline, such as carbon content of fuel used, capital costs, fuel prices and discount rate (Lee et al., 2005). Such assumptions could introduce uncertainty in the calculated baselines; therefore identifying such uncertainties and/or assumptions enables a more transparent methodology and verifiable analysis. Conservatism implies such assumptions and parameters adopted by the baseline emissions are on the lower rather than the higher side. Conservatism should also be adopted while determining key parameters and transparently stating the assumptions used to project the baseline (Lee et al., 2005). Conservativeness in estimating GHG credits must also be observed in selling and buying net emission reductions, to avoid manipulation of baselines or establishing artificially high baselines (Gustavsson et al., 2000).
It is also important to consider secondary effects arising from the mitigation project, for example in a forest conservation project - if the wood fuel produced was from the mitigation project and if the fuel was used to replace fossil fuels (Gustavsson et al., 2000). Therefore, mitigation projects effecting changes in GHG emissions have to carefully define the system boundaries or key parameters, both in space and time (Gustavsson et al., 2000; Lee et al., 2005). Baselines should also be broadly practical and moderately simple in their application to a variety of places or contexts. They should be verifiable for other project managers, stakeholders, investors, or impartial third parties in the interest of the achieving the UNFCCC’s objectives (Gustavsson et al., 2000). The practicality of a baseline can be increased by setting clear, transparent and comparable standards that can be used across similar types of mitigation projects (Gustavsson et al., 2000).
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2.4.1 Different Types of Baselines
Typically, there are four types of baselines: 1) project-specific, 2) multi-project, 3) hybrid, and 4) top-down baselines (refer Figure 2.3) (Ellis & Bosi, 2000; Gustavsson et al., 2000; Sathaye et al., 2004). Additionally, the two common methods of measuring overall building stock energy consumption performance, and associated GHG emissions and mitigation potential are the top-down and bottom-up approaches (Barker et al., 2007a; Kavgic et al., 2010). Regardless of the different types of baselines, a baseline is to an extent project-specific, in determining the degree of magnitude of what is being measured (e.g. area of reforestation, capacity of biomass-fired plants) (Gustavsson et al., 2000). A top-down approach tend to utilize national aggregated of energy consumption, while a bottom-up approach requires sectoral or disaggregated empirical data to calculate its associated GHG emissions or mitigation potential (Barker et al., 2007a; Kavgic et al., 2010).
Project-specific Bottom-Up
Static Multi-project Baselines Dynamic Top-Down
Hybrid
Figure 2.3 Types of Baselines Adapted from Gustavsson, et al., (2000) and Ellis & Bosi (2000)
Baselines can also be categorized as either static (fixed duration of the project’s lifetime), or dynamic (revised at a constant level throughout the project operation) (Ellis & Bosi, 2000; Gustavsson et al., 2000). Static baselines are often predictable or simulated, therefore provides a level of certainty in calculation (Ellis & Bosi, 2000). Static baselines are also often more cost effective, in lieu of only one estimation for the project, which requires less administration, monitoring and reporting (Ellis & Bosi, 2000).
In comparison, the dynamic baseline is reassessed or re-estimated at certain intervals to reflect closely the changing or continuing additionality of a project, therefore increases
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transaction costs (Ellis & Bosi, 2000). Additionality can be defined as the emissions reduction of a project that is additional relative to the baseline, in other words, emissions reduction through non-business-as-usual projects (Ferrey, 2010; Yunna & Quanzhi, 2011). Dynamic baselines allow a baseline to be adjusted if the performance of the project improves and are therefore more accurate than static baselines, (Ellis & Bosi, 2000). Continuous monitoring shows the historic moving baseline to allow more accurate projections and gaining more accurate credits (if available).
However, the changing nature of a dynamic baseline increases the uncertainty of a projected or estimated outcome in the future (Ellis & Bosi, 2000). Baselines are commonly used as a starting point for assessment, and can be measured against another variable. Calculation for GHG emissions reduction in mitigation projects requires a baseline which sets emission levels projected to occur without the project (Sathaye et al., 2004).
2.4.2 Bottom-Up Baselines
A bottom-up approach in generating baseline provide a comprehensive representation of specific technologies within the energy system (Lanz & Rausch, 2011). Bottom-up approach analysis provides mitigation options on specific technologies at a regional or sectoral level, and requires disaggregated data (Hoogwijk et al., 2009; Lanz & Rausch, 2011). Moreover, bottom-up approaches rely on detailed analysis and data of end-use energy services (Hourcade & Robinson, 1996; Tsuji et al., 2004). Both project-specific and multi-project baselines fit into the bottom-up approach, where both require specific disaggregated data in developing each baseline (Moura-Costa et al., 2000 cited in Ravindranath & Ostwald, 2008).
A project-specific baseline is determined by project-specific measurements or assumptions for its key considerations while a multi-project baseline is measured and aggregated by its activity (Ellis & Bosi, 2000; Gustavsson et al., 2000). A project-specific baseline often involves high transaction costs, which requires high concentrations of disaggregated data that consequently disadvantages small-scale projects (Ellis et al., 2001; Sathaye et al., 2004; Shrestha & Abeygunawardana, 2007). This is due to the specific project data (as opposed to a collection of comparable projects) needed for all key parameters, such as fuel and technology characteristics, and the changes of each parameter over the lifespan of the project (Ellis & Bosi, 2000; Gustavsson et al., 2000).
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This conversely makes a project-specific baseline easier to monitor and verify, as it consists of smaller parameters of measurement as compared to multi-project baselines (Ravindranath & Ostwald, 2008). Additionally, the methodology used to develop a project-specific baseline is transferable to other projects of parallel nature (UNEP, 2005). According to the Marrakech Accord’s39 guidelines in establishing baselines, it insists on project-specific baselines for its CDM mechanism (Lee et al., 2005). This underlines the importance of project-specific baselines in policy development and climate change mitigation mechanisms.
However, there is a high level of uncertainty in project-specific baselines, due to their hypothetical nature and their assumed or simulated key parameters (Ellis & Bosi, 2000; Ravindranath & Ostwald, 2008). Consequently, a uniform reporting format will help reduce the inconsistency and increase transparency of project-specific baselines (Ellis & Bosi, 2000). The UNEP-SBCI’s Common Carbon Metric allows for a uniform reporting format, which then produces measurable and verifiable data which will help increase accuracy for project-specific baselines.
Similar to project-specific baselines, multi-project baselines are also calculated based on emission rate assumptions (gCO2/kWh) or absolute levels, but are designed to standardize the emission levels for multiple projects of similar type concurrently (Ellis & Bosi, 2000). Multi-project baselines use existing or estimated emissions from a predetermined set of actual or planned standard baseline to which multiple projects can be compared in parallel (Sathaye et al., 2004). Multi-project baselines involve a broad range of baseline categories, which encompass different levels of geographical or sectoral aggregation, but could also based on historical data, simulation or projected trends (Ellis & Bosi, 2000).
A multi-project baseline is often used as an alternative to a project-specific baseline to cut transaction costs, if multiple projects are run concurrently (Sathaye et al., 2004). It may also be more consistently transparent as multiple stakeholders take part in the decision-making process (Sathaye et al., 2004). However, the additionality of each project under multi-project baselines are often not assessed and highly debatable
39 Electronic copy can be found at http://cdm.unfccc.int/Reference/COPMOP/decisions_15_17_CP.7.pdf
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(Sugiyama & Michaelowa, 2001), which jeopardizes the effectiveness of the project/s (Ellis & Bosi, 2000; Zhang & Wang, 2011).
2.4.3 Top-Down Baselines
A top-down baseline is often referred to a national level baseline, which is highly aggregated and is used to reflect national or regional objectives/targets, or to assess emission reductions from policy initiatives (Ellis & Bosi, 2000). Top-down baselines are not project-activity sensitive, and are based on highly aggregated country-specific data (Ellis & Bosi, 2000; Gustavsson et al., 2000). Top-down baselines generally represents wide-sector economic activities through aggregated data (Hoogwijk et al., 2009; Lanz & Rausch, 2011; Sue Wing, 2008).
Top-down baselines can also be statistically compared against a similar representative samples of individual project’s performance (bottom-up), in order to compare the performance of regional and national statistics (UNEP-SBCI, 2010b). A top-down baseline is usually based on limited statistical data entries on a large scale, which reflects the national average for certain emission sources, but not necessarily the actual production or activity which causes the emissions (Bader & Bleischwitz, 2009). Therefore, bottom-up calculations complement the top-down average, in ensuring a more accurate representation of the GHG emissions inventory or baseline, in respect to the sources of emissions (Bader & Bleischwitz, 2009).
The major difference between the top-down and bottom-up approaches largely depends on the domains in which they operate, i.e. the top-down is an economy-wide representation, versus the bottom-up specific systems boundary (Sue Wing, 2008). Top- down models may not be able to detect discontinued technological changes and these implicating factors may not be described explicitly within the models (Kavgic et al., 2010). Also, top-down models are mainly influenced by a range of factors such as structural change and saturation effects (Kavgic et al., 2010). In terms of producing energy models, the top-down approach simulates the ‘supply and demand’ interactions within the economy, for all commodities including energy and non-energy activities (Sue Wing, 2008).
Conversely, the bottom-up approach simulates the interaction between numerous single or disaggregated components that are then combined to form the energy system within
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the economy (Kavgic et al., 2010; Sue Wing, 2008). Efforts in reconciling the top-down and bottom-up approaches have produced hybrid models, which incorporate bottom-up technological details with the top-down macroeconomic structure (Sue Wing, 2008). Hybrid models generally consists of two modelling components to estimate energy consumption: statistical from the top-down approach, and physically-based data from the bottom-up method (Kavgic et al., 2010).
The CCM’s top-down approach measures energy consumption and associated GHG emissions for the building stock using national gross energy consumption, and building stock profiles such as gross floor area, age, building type and occupancy (UNEP-SBCI, 2010b). This research focuses only on the top-down and bottom-up approaches provided in the CCM to generate a baseline. This will be further elaborated in Chapter 4.
2.4.4 Hybrid Baselines
A hybrid baseline is usually designed for projects that do not quite fit into a project- specific or multi-project characteristics, and is determined with or without other standards (Ellis & Bosi, 2000; Gustavsson et al., 2000). Hybrid baselines are often more aggregated and standardized than project-specific baselines, but less than multi-project baselines (Ellis & Bosi, 2000). Implementing a hybrid baseline would not necessarily produce identical results because of project-specific variations in its different parameters and circumstances (Ellis & Bosi, 2000). However, it can still be used to reduce the wide divergence of similar projects undertaken in different countries or circumstances (Ellis & Bosi, 2000).
2.5 Existing Methods for Measuring the Building Sector’s Climate Impact
Higher living standards and increasing material affluence in emerging economies have been shown to be a cause of global ecological degradation through energy and electricity consumption (Wee et al., 2007). According to the IPCC’s Fourth Assessment Report, the building sector’s (residential and commercial) energy-related GHG emissions were 8.6 Gt/yr, almost a quarter of global GHG emission, and grew at an annual rate of 2% from 1971 to 2004 (Levine et al., 2007). GHG emissions for residential buildings grew 1.7% annually while commercial buildings grew 2.5% annually (Levine et al., 2007).
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Energy consumption in the building sector varies according to function, culture, climate, typology, building envelope and design (Huovila et al., 2009; Sisson et al., 2009). For instance, a typical U.S. commercial building consumes approximately 12% of energy for space heating, and 10% for space cooling (Levine et al., 2007; Sisson et al., 2009). In comparison, China’s commercial buildings typically consume approximately 45% for space heating and 14% for space cooling (Levine et al., 2007). Japan’s commercial building energy consumption for space heating is lower than China’s, at 29% and 14% for space cooling (Levine et al., 2007; Sisson et al., 2009).
Residential buildings in the U.S. typically consume more energy for space heating than commercial buildings, at approximately 29% of total energy consumption (Levine et al., 2007). However in comparison, an average residential building in China consumes less energy for space heating than commercial buildings, at 32% of total energy consumption (Levine et al., 2007). Therefore, it is necessary to make changes to the way buildings consume energy, especially in the developing world where the majority of the world’s new construction is taking place (Hong et al., 2007). The growing concern for reducing the building sector’s detrimental impact on the environment has also consequently developed many eco-labelling, green building rating schemes, and environmental building performance assessment tools (Cole, 1999; Ding, 2008; George, 2009; Watson, 2009).
2.5.1 Building Environmental Assessment (BEA) Tools
The Building Research Establishment Environmental Assessment Method (BREEAM) was established in 1990 in the UK and was the first commercially available environmental assessment tool for buildings (Building Research Establishment (BRE) Global, 2009; Crawley & Aho, 1992; Watson, 2004). Following the BREEAM tool, many other building environmental assessment (BEA) tools have been developed around the world, for different purposes and different scales (Haapio & Viitaniemi, 2008). Other well known rating tools such as the EcoProfile in Norway, Leadership in Energy and Environmental Design (LEED) in the United States, Green Star in Australia, Green Mark in Singapore and the Comprehensive Assessment System for Built Environment Efficiency (CASBEE) from Japan and Building Environmental Assessment Method Plus (BEAM Plus or formerly known as HK-BEAM) in Hong Kong (Cole, 2005; Ding, 2008; Haapio & Viitaniemi, 2008; Lee, 2012; Papadopoulos & Giama, 2009) are credit-based rating tools.
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The BREEAM assessment and rating tool has certified over 200,000 buildings in the United Kingdom, and the LEED rating system has expanded its projects to other countries like China, Brazil and India (Managan et al., 2012). A Chinese public-private partnership developed the Three Star Rating System applicable to commercial buildings, in efforts to promote more green construction in China (Managan et al., 2012). The Green Mark rating tool in Singapore has been embedded as one of the key criteria in its Building Control (Environmental Sustainability) Regulations (BCA, 2008; UNESCAP, 2010).
Energy performance building assessment can be classified into two major categories, either performance-based or feature-specific approach (Wang et al., 2012). Performance- based tools assess the energy performance of a building and allows for a more precise quantification of performance indicators such as CO2 emissions or kWh (Lee, 2012; Wang et al., 2012). However, assessment methods for a performance-based tool is more difficult as it requires the establishment of appropriate method and criteria to quantify and evaluate the building’s energy performance (Wang et al., 2012). The BREEAM tool can be classified as a performance-based tool as it sets a minimum and maximum performance level criteria, in terms of its GHG emissions (Lee, 2012).
Feature-specific rating tools credit a building and rate the predicted post-design performance of whole buildings based environmental weightings on an overall scale (Ding, 2008; Haapio & Viitaniemi, 2008; Papadopoulos & Giama, 2009). These rating tools grades buildings according to energy efficiency design solutions according to a pre- assigned scale, rather than addressing actual environmental performance during building use (Pushkar et al., 2005). These tools assess building performance either by quantitative performance indicators using points, or ranking systems such as platinum, gold or silver, or using stars (Ding, 2008; Retzlaff, 2008). LEED and CASBEE adopts a feature-specific assessment criteria and energy budget mechanisms for its energy efficiency approach (Lee, 2012). While design based tools are important, the more significant issue is to assess actual building performance during its operational stage, as this stage represents approximately 90% of the building life cycle’s emissions (UNEP- SBCI, 2010b). Refer Table 2.3 for a brief summary of existing BEA tools.
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Building Environmental Assessment (BEA) Tools Key Features BREEAM BEAM Plus LEED CASBEE Green Mark Green Star First launched United Kingdom (1990) Hong Kong United States of America Japan Singapore Australia (1996) (1998) (2001) (2005) (2002) Implementation Partially mandatory Voluntary Partially mandatory Voluntary Mandatory for new Voluntary approach buildings Building types New construction of: New and existing of: New construction, New construction, New construction New construction industrial; office; retail; residential; existing building and existing building and and existing for: non- for: education, homes; multi-residential; commercial; and mixed major renovation for: renovation for: residential buildings, healthcare, healthcare; courts; use complexes. homes, schools, retail, detached house and residential, industrial, multi- prisons; education; and commercial interior, other building type; restaurants, parks; unit residential, communities. Existing or core and shell, temporary Existing: schools, office, office in-use for: all non- hospitality, data centres, construction; local data centres, retail, interior, and retail domestic (commercial, warehouse; and government; heat and infrastructure. centre industrial, retail and neighbourhood island; cities and urban institutional) development. development Environmental Management; health & Site; materials; energy Sustainable site; water Global warming; air Energy efficiency; Management; criteria/Issues wellbeing; energy; use; water use; and efficiency; energy & pollution; heat island water efficiency; IEQ; energy; covered transport; water; indoor environmental atmosphere; materials & effect; load on local environmental transport; water; materials; waste; land quality (IEQ). resources; IEQ; infrastructure; noise; protection; IEQ; and materials; land use & ecology; pollution; innovation in design; and vibration & odour; other green features. use & ecology; and innovation. regional priority. wind damage & emissions and sunlight obstruction; innovation. and light pollution. Scoring scale Points Points Points Ratio Point Point (maximum (122) (143) (110) (190) (143) point) Level of award Pass; good; very good; Bronze; silver; gold; Certified; silver; gold; Poor; fairly poor; good; Certified; gold; gold 1-5 Stars excellent; outstanding. platinum. platinum. very good; excellent. plus; platinum
Table 2.3 Brief Summary of Building Environmental Assessment (BEA) Tools Source: (BCA, 2013; BRE Global, 2012; GBCA, 2013; JaGBC, 2013; Ng et al., 2013; USGBC, 2013)
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2.5.2 The Need for a Universal Method of Measuring GHG Emissions from Building Operation
Most of these BEA tools have included emissions accounting into their rating systems, such as BREEAM, LEED, CASBEE, Green Mark and Green Star (Ng et al., 2013). However, these emissions assessment vary from tool to tool, due to different environmental regulations, accounting methodology and building life-cycle phase (refer Table 2.4) (Managan et al., 2012; Ng et al., 2013). These building tools cater mostly for individual buildings, and are designed to be used in their country and/or region and climatic conditions (Ng et al., 2013; Reed et al., 2009). These rating tools are also implemented on different levels as a voluntary or partially mandatory mechanisms, and have different environmental criteria (Ng et al., 2013).
Stages Concerning Carbon Emissions Evaluation Building Environmental Assessment (BEA) Tools Pre-Construction Operational Renovation End-of-Life
BREEAM ***
BEAM Plus * ***
LEED ** ***
CASBEE ** ** ** **
Green Mark ***
Green Star ** ***
Note:
* Qualitative assessment of CO2 emissions performance
** Quantitative assessment of CO2 emissions performance
*** Both qualitative and quantitative assessment of CO2 emissions performance
Table 2.4 Building Life-Cycle Phase regarding Emissions Assessment Source: Ng, et al.(2013)
Additionally, these rating tool do not use the same level of comparability or a consistent metric internationally (Reed et al., 2009; UNEP-SBCI, 2010b). These BEA rating tools also rely heavily on simulated, not measured performance (Ng et al., 2013), and have not generally focussed on low-cost or affordable housing. Therefore, a standardized format to evaluate GHG emissions from buildings is needed to continuously promote and monitor emissions reductions from this important sector (Ng et al., 2013).
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A Malaysian version of an building environmental assessment tool is the Green Building Index (GBI), which was developed in 2009 that evaluates buildings on a 100–point scale weighted against its performance areas of energy efficiency, indoor environmental quality, sustainable site planning and management, materials and resources, water efficiency, and innovation (Greenbuildingindex, 2012b). The current tool has no references to measuring GHG emissions from building operation. GBI assessment tool mainly focuses on new construction; whereby out of four areas of application, three are to new construction, and one for existing building (Greenbuildingindex, 2012b). This will be further elaborated in the Malaysian building sector section.
The existing BEA tools are not harmonized methodologically, in terms of their reporting frameworks i.e. rating scale, criteria and evaluation phase. Therefore the gap in current research and development in BEA tools is the need for a consistent measureable, reportable and verifiable (MRV approach) in order to enable a more effective comparison of building sector’s energy performance.
2.5.3 Existing GHG Accounting Tools
Beyond individual buildings, there are reporting tools to measure and report building stock or industry wide GHG emissions and/or environmental footprint. These include Carbon Disclosure Project (CDP), the National Australian Built Environment Rating System (NABERS), the Greenhouse Gas (GHG) Protocol, the International Organization for Standardization (ISO) 14064: Greenhouse Gases, and Greenprint Performance Report, (APREA, 2013; Chomkhamsri & Pelletier, 2011; Fransen et al., 2010; Greenprint, 2011; OEH, 2011a).
The Carbon Disclosure Project is non-profit organization that provides a global system to measure and report GHG emissions and climate change risk for private organizations and cities (Carbon Disclosure Project, 2013a). The CDP is mainly divided into two reporting frameworks, i.e. for companies/organizations to report their environmental impact on climate change, supply chain, water and forests, and cities in reporting its GHG emissions and climate change risk (Carbon Disclosure Project, 2012, 2013a). Both of the reporting framework include measuring and reporting on buildings, as contributor
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of GHG emissions (for Scope 1 and/or Scope 2 emissions type40) (Carbon Disclosure Project, 2013b, 2013c).
However, emissions reporting methodology differ based on individual companies/organizations and cities in calculation their respective Scope 1 and Scope 2 emissions (Carbon Disclosure Project, 2013b, 2013c). There is a wide range of reporting methodologies and protocol accepted by CDP, to measure individual organizations or cities GHG emissions (refer Appendix 2.1), and at present CDP’s overall scoring methodology do not differentiate between methodologies or protocols used and reported by individual organizations or cities (Carbon Disclosure Project, 2013b, 2013c). The CDP mainly collates and reports on a global scale based on information given by individual organizations and cities, but does not adhere to a specific reporting methodology.
Conversely, NABERS is a Australian national environmental rating system that is managed by the New South Wales state government, at the Office of Environment and Heritage (OEH) (OEH, 2011a). Unlike most BEA tools, NABERS measure actual and annual operational performance of buildings, using measured performance data such as energy or water bills, or waste audits (OEH, 2013). NABERS star rating system is also measured using benchmarked data of similar buildings within the same location (OEH,
2013). NABERS energy star ratings are based on GHG emissions (kgCO2/m2) that is normalized and converted from operational energy consumption (Bannister, 2012; OEH, 2011c).
NABERS is categorized into four (4) rating tools: energy, water, waste and indoor environment, and is applicable to five (5) building type: date centre, home, hotel, office and shopping centre (OEH, 2013). For office buildings, NABERS allow for three (3) types of performance measurements: tenancy, base building or whole building (OEH, 2013). Tenancy rating allows measurement for energy/resources consumed or indoor environment of the space rented and controlled by the tenant (OEH, 2013). Conversely, base building rating measures operational performance of common building services and areas that is outside tenanted spaces, and is usually managed by the building owner (OEH, 2013). The whole building rating measures both tenanted spaces and base building performance, and is preferable for owner-occupied buildings or when inadequate measurement for either tenancy or base building rating (OEH, 2013).
40 Refer Chapter 4 for definition of Scope 1, 2 and 3 emission type (under Life Cycle Boundary Subsection)
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NABERS has since been adopted as a sustainability standard for government owned and leased offices (OEH, 2011b). NABERS star rating is used as a minimum standard, differing according to each State Government or the Commonwealth Government, for instance in New South Wales, there is a minimum of 4.5 energy stars rating for office spaces over 1,000m2 (OEH, 2011b). Most governments have already adopted a minimum of 4.5 energy star rating for office spaces, except for Tasmania (as of July 2011) (OEH, 2011b).
As NABERS remain essentially a rating tool, which produces star rating against weighted energy or resource consumption, it would be difficult in establishing a baseline data for building stocks that are internationally comparable. However, the collective NABERS ratings data has since been used by the Australian Government Department of Climate Change and Energy Efficiency (DCCEE) to provide a national baseline of energy consumption and GHG emissions for commercial buildings (DCCEE, 2012).
Contrary to NABERS and CDP, the Greenhouse Gas (GHG) Protocol is an international GHG emissions accounting tool that was convened in 1998 by the World Business Council for Sustainable Development (WBCSD) and the World Resources Institute (WRI) (GHG Protocol, 2012). The GHG Protocol has developed four (4) separate but connected standards and three (3) categories of calculation tools (GHG Protocol, 2012):
Standards 1) Corporate Accounting and Reporting Standards (Corporate Standard); 2) Project Accounting Protocol and Guidelines (GHG mitigation projects); 3) Corporate Value Chain (Scope 3) Accounting and Reporting Standards; and 4) Product Life Cycle Accounting and Reporting Standards
Calculation Tools 1) Cross Sector Tools; 2) Sector Specific Tools; and 3) Customized Calculation Tools.
The GHG Protocol’s Corporate Standard, provides guideline for a consistent and transparent GHG accounting and reporting methodology that can be used governments and businesses alike (GHG Protocol, 2012). The main objectives of the Corporate
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Standard is to assist businesses or organizations in establishing a GHG inventory and to increase consistency and transparency in GHG accounting via standardized approaches and protocols (GHG Protocol, 2001). The GHG Protocol Corporate Standard guides businesses or organizations to identify which Scope of emissions to account for (i.e. Scope 1, 2 or 3) and to apply which suitable calculation tool (i.e. cross-sector tool, sector- specific tool, or the customized calculation tool) (GHG Protocol, 2001). The GHG Protocol calculation tools are consistent with the IPCC’s emissions calculation41 at a national level and have been refined to organizational-based level to encourage companies for accountable GHG emissions reporting (GHG Protocol, 2001).
The GHG Protocol Corporate Standard prescribes to two (2) approaches in gathering data to generate a GHG inventory, via ‘centralized’ and ‘decentralized’ approaches (GHG Protocol, 2001). Centralized approach allows for activity data from individual facilities within an organization to be calculated at a central or corporate level, to ensure a standardized emissions calculation across the organization (GHG Protocol, 2001). The opposite occurs within a decentralized approach, where individual facilities within an organization calculates its own GHG emissions and reports it to the central or corporate level (GHG Protocol, 2001). A decentralized approach could increase risk for calculation errors, but is preferable for organizations consisting of various facility types or when local regulations require GHG emissions at facility level (GHG Protocol, 2001).
The GHG Protocol’s Corporate Standard has since been adopted by the International Organization for Standardization (ISO) for its ‘environmental management series’, as its accounting and reporting methodology in ISO 14064-1:2006 - Specification with Guidance at the Organization Level for Quantification and Reporting of Greenhouse Gas Emissions and Removals (GHG Protocol, 2012; ISO, 2010). The ISO 14064-1:2006 prescribes requirements and principles for organizations, in quantifying and reporting GHG emissions and removals that includes design requirements, development management, reporting and verification of its GHG inventory (ISO, 2010).
Similar to the Carbon Disclosure Project, Greenprint is a international non-profit organization that works with the real estate industry (i.e. owners, investors, financial
41 IPCC’s “simple methodological approach is to combine information on the extent to which a human activity takes place (called activity data or AD) with coefficients which quantify the emissions or removals per unit activity. These are called emissions factors (EF)”. In other words, Emissions = AD x EF (IPCC, 2006, p. 6)
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institutions) to help promote strategies in reducing GHG emissions and energy consumption (Greenprint, 2011). Greenprint Performance Report prescribes to energy data sources that are based on “utility bills, invoices, power supply company records or meter readings” (Greenprint, 2011, p. 29). The Greenprint reports on (5) types of property: office, industrial, retail, multifamily and hotel/lodging (Greenprint, 2011).
However, unlike the CDP, Greenprint has adopted a standardized reporting methodology for its reporting members that is similar to the GHG Protocol, in determining scope of emissions (Scope 1,2 or 3) (Greenprint, 2011). The Greenprint Performance Report have also adopted a similar formula to IPCC in calculating GHG emissions: GHG emissions (kgCO2e) = Energy (kWh) x Emissions Factor (kgCO2e/kWh) (Greenprint, 2011). The collective Greenprint performance data have been adopted by Deutsche Asset & Wealth Management (DAWM), or formerly known as RREEF Real Estate, in reporting its annual Real Estate Sustainability Report (DAWM, 2013; RREEF, 2012).
This section indicates that existing GHG accounting tools can improve its consistency by standardizing a common reporting method. The ISO 14064-1 and Greenprint have adopted GHG Protocol’s emissions accounting method into its reporting framework, which is originally based on IPCC’s emissions formula. UNEP-SBCI’s Common Carbon Metric and Protocol (CCM) in another type of GHG accounting tool that diverged away from GHG Protocol, to only report on buildings (GHG Protocol, 2010).
There is a need to implement such tools into the developing country context, as half of the world’s construction is occurring in urban areas of developing countries. Most of the existing BEA and GHG accounting tools are designed to work within a developed country framework and for countries with seasonal climates, or to work as a typical simulation without regards to specific location and/or climate.
Malaysia is a developing country with rapid urban development and located in the tropical climate region. These features were found most adaptable with the CCM as it only focuses on calculating GHG emissions through conversion of energy source. Additionally, the Phase 2 CCM had added climate data section into their emissions calculator, in terms of cooling degree days and heating degree days to normalize building energy performance based on the climatic conditions of where the building is
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located (UNEP-SBCI, 2010b). Adding climate data also allows a more “meaningful comparison of performance of different buildings located in different places, or of the same building year after year” (UNEP-SBCI, 2010b, p. 57).
2.5.4 The UNEP-SBCI’s Common Carbon Metric as Universal Tool to Measure Operational GHG Emissions
The main objective in developing consistent data is to inform and develop policy for GHG mitigation from buildings, especially in developing countries where such policies do not exist (UNEP-SBCI, 2010b). The UNEP developed ‘Common Carbon Metric’ is designed to measure and report on greenhouse gas (GHG) emissions related to building operation, which also enables baseline GHG emissions per meter square (m2) to be calculated based on aggregation of buildings of the same typology (stock aggregation) (UNEP-SBCI, 2010b). The goal of CCM is “to provide globally applicable common metrics for measuring and reporting energy use in GHG emissions from existing building operations to support international, regional, national, and local policy development and industry initiatives” (UNEP-SBCI, 2010b p.3).
The CCM is a tool for collating energy use data from electricity bills and calculating indirect GHG emissions. The CCM also provides universality, using its protocol to generate baseline emission data from limited or minimal information, but from variety of sources. The CCM measures consistent and comparable energy intensity (kWh/m2/year – Kilowatt hours per square meter per year) and carbon intensity
(kgCO2e/m2/year or kgCO2e/o/year – kilograms of carbon dioxide equivalent per square meter or per occupant per year) (UNEP-SBCI, 2010 p. 9). The CCM’s methodology is also consistent with internationally agreed principles for environmental performance assessments, such as the International Organization for Standardization (ISO) and GHG Protocol developed by WRI and WBCSD (Gupta, 2011; UNEP-SBCI, 2010b). The CCM is not a BEA tool whereby no weightings or credit is associated, but it presents measurements of GHG emissions that is consistent and comparable with the international framework mentioned above.
According to the UNFCCC’s methodological tool to calculate baseline emissions from electricity consumption, emissions from electricity consumption include the GHG emission from the combustion of fossil fuel at power plants, either at the site or from electricity grid systems (UNFCCC, 2008). Calculating of emission from electricity
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consumption are based “on the quantity of electricity consumed, an emission factor for electricity generation and a factor to account for transmission losses” (UNFCCC, 2008 p.3).
Assessing a building’s performance through its energy related emissions provides an indicator of post-construction and operational impact on the environment. This consequently provides a foundation to draw accurate performance baselines, which can help set national targets and potentially be utilized for carbon trading (UNEP-SBCI, 2010b). Consistent data can also be used to support the formulation of Nationally Approved Mitigation Action (NAMA) plans that were introduced in 2007 at the United Nations Climate Change Conference in Bali (UNEP-SBCI, 2010b).
Without baselines, presenting measurable climate change benefits and emissions reduction impact would be unattainable (UNEP, 2009). Investigating GHG emissions from the existing building stock consequently provides a performance baseline for policy development in reducing the industry’s emissions (UNEP-SBCI, 2010). Generating performance baselines helps to inform policymaking, MRV quantitative data is presented (UNEP-SBCI, 2010). Therefore, as current BEA tools are not consistently comparable, adopting the CCM for this research is appropriate.
The CCM’s bottom-up approach also measures actual performance of individual buildings, or groups of buildings, using utility data of purchased and metered electricity (UNEP-SBCI, 2010b). Furthermore, the CCM’s calculation of GHG emissions is similar to UNFCCC’s equation that calculates emissions from quantity of consumed electricity (MWh or kWh) multiplied with the emissions factor for electricity generation (MWh or kWh)42 (see Equation 2.1).
The CCM was first piloted during 2010 by nine (9) international organizations, covering 46 individual buildings with a total area of 1.44 km2 and five (5) building typologies (total area of 176.60 km2) (Gupta, 2011; UNEP-SBCI, 2010c). The CCM Phase I case studies presented in a wide variation of building type and scale, from single office buildings to city level building stock (UNEP-SBCI, 2010d). The CCM Phase I included case studies from Germany, France, Sweden, United States, Sydney, Singapore, South
42 However, it must be noted that the CCM’s GHG emissions calculation does not include the average transmission and distribution losss for electricity production (UNEP-SBCI, 2010b).
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Africa, and India. The CCM’s Phase I primary challenge was to develop a consensus- based definition for key terms, such as building area and building type, for consistent measurement and representation over the heterogeneous building sector and across different regions (UNEP-SBCI, 2010c).
Where: = Project emissions from electricity consumption in year y (tCO2/yr) = Quantity of electricity consumed by the project electricity consumption source j in year y (MWh or kWh/yr) = Emission factor for electricity generation for source j in year y (tCO2/MWh or kWh) = Average technical transmission and distribution loses for providing electricity to source j in year y Equation 2.1 Generic Approach for Project Emission from Consumption of Electricity Source: UNFCCC (2008) p.3
Addressing this challenge, the CCM refined its definitions for building area and building type (refer Appendix 2.2 for definition) for the Phase 2 pilot testing. It must be noted that this research was part of the CCM Phase 2 pilot testing project, focusing on multi- family residential buildings (also refer Appendix 2.2 for definition) (Gupta, 2011). This will be further explained in the ‘Piloting the Case Study’ section in Chapter 4.
2.5.5 Predicted and Measured Performance
There has been extensive research in understanding the energy performance gap between predicted and measured performance and the evidence suggests that buildings generally do not perform as well as predicted (Bordass & Leaman, 2005; Menezes et al., 2012; Way & Bordass, 2005). Some studies also indicate that measured energy consumption in buildings can be twice as much as the prediction (Bordass et al., 2001). The main source that causes discrepancy between predicted and measured energy performance are design assumptions, modelling tools, commissioning, management and control, occupant behaviour, and construction quality (Ignacio Torrens et al., 2011; Menezes et al., 2012). Design assumption, modelling tools and commissioning affect predicted energy performance through errors and/or assumptions, while management and control, occupant behaviour and construction quality affect measured or actual energy performance of buildings (Ignacio Torrens et al., 2011; Menezes et al., 2012).
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Predicted or simulated energy performance is mainly based on assumptions and/or without accurate description of the building context, which presents oversimplified and unrealistic results (Menezes et al., 2012). Design assumptions are assumptions made to predict the building energy performance and are usually made during the design stage where numerous building features and functions are still uncertain or finalized (Menezes et al., 2012). These early assumptions affect the future prediction of construction quality and building performance, occupant behaviour and management of the building systems (Menezes et al., 2012).
Similarly, modelling tools might have errors embedded in the modelling tool that risks the accuracy of prediction. Such errors occur when the modelling tools only allow generic energy performance modelling without specific considerations towards typology, location, climate and so forth, which might not provide an accurate representation of reality (Menezes et al., 2012). Progressive development on modelling tools try to increase the complexity of simulation software to include a more specific or dynamic data set, such as dynamic heat transfer prediction for thermal modelling software (Menezes et al., 2012). However, existing simulation or modelling tools are still unable to accurately predict the impact of operations management and occupant behaviour have on overall building energy performance (Menezes et al., 2012). This is further exacerbated by the lack of feedback between actual and operational energy use into the development of modelling software (Menezes et al., 2012).
Building commissioning is meant to ensure building systems to be designed, installed and tested with high quality for its operational stage (Ignacio Torrens et al., 2011). However, the commissioning process of identifying performance problems through analysing measured data is very time consuming, and most simulation models used are automated to analyse data based on typical building systems such as heating, ventilation and air conditioning (HVAC that do not necessarily reflect actual performance of the installed HVAC system during building operation (Maile et al., 2012). As HVAC systems are the main building systems that usually do not perform as well as predicted during operation (Maile et al., 2012), it is important to consider progressive performance measurement to ensure optimum performance during operational phase.
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Therefore, management of building systems are very important to determine if actual performance is operating as prescribed in the simulated models. Conversely, inefficient or inappropriate strategies lead to unnecessary energy waste (Menezes et al., 2012). Energy audits can also help maximize efficiency of building systems and avoid unnecessary energy waste (Menezes et al., 2012), through determining areas of high energy consumption and strategize for a more energy efficient or energy saving solutions.
Equally important if not more so than energy efficient building systems, occupant behaviour greatly impacts actual energy consumption in building operation. Occupant behaviour affects energy performance of a building through actions within their individual internal environment, such as opening of windows, wastage of lighting and ventilation systems unnecessarily, and blocking air inlets/outlets (Menezes et al., 2012). Additionally, occupants have individual control over ‘unregulated items’ that are not within jurisdiction of Building Regulations, such as computers, printers, and other energy consuming appliances/equipment (Menezes et al., 2012).
Another important factor that affects measured energy performance of a building is its construction quality (Ignacio Torrens et al., 2011; Menezes et al., 2012). The quality of insulation and thermal properties of buildings, commonly gaps within walls or windows, affects its energy consumption with additional cooling and/or heating load needed to maintain a comfortable indoor temperature (Menezes et al., 2012). The energy gap between measured and predicted energy consumption can be reduce through analysing or comparing measured and simulated data on building systems (Maile et al., 2012) and with a feedback cycle of measured performance into the development of modelling tools (Menezes et al., 2012).
However, existing comparison methods are generic and do not assess measured performance of specific systems or tasks within the building operation (Maile et al., 2012). Assessing the energy performance of various levels of building system are needed, and should allow data hierarchy in order to collate the relationships of various interdependent building systems (Maile et al., 2012). An Energy Performance Comparison Methodology (EPCM) was developed to identify performance problems during the operational phase, and compare it to simulated data using building performance energy simulation (BEPS) model (Maile et al., 2012). The EPCM was
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developed in extension of three (3) analyses: measuring building performance, simulating building energy performance, and comparing the measured and simulated data (Maile et al., 2012). The BEPS models generated during the design stage act as a benchmark, in comparison of the actual performance data (Maile et al., 2012).
Correspondingly, one of the methods used to provide a feedback cycle between measured and predicted energy performance is by conducting a post-occupancy evaluation (POE) (Menezes et al., 2012). A POE is a evaluation of the building performance after construction and has been occupied (Menezes et al., 2012). It can further be divided into three (3) categories, i.e. feedback, feed-forward and benchmarking (Menezes et al., 2012). The feedback categories is used as a mechanism to measure building performance as a business and organizational efficiency, whereas feed-forward supplies acquired building performance data to the design team to improve building procurement for the future (Menezes et al., 2012). Benchmarking is used as a mechanism to measure the progressive performance of a building in achieving its targeted goals (Menezes et al., 2012).
Therefore collecting measured and actual building energy performance is significant, to both report on actual representation of reality and the development of more accurate modelling tools. Without measured data, it would be very difficult to ensure energy efficient systems are performing as they were intended to, and to close the energy gap between predicted and measure performance. This research focuses on collecting measured energy performance of public low-cost housing, using purchased electricity bills to help address this issue and build a database for the low-cost housing typology as the least environmentally research typology in Malaysia. Consequently the measured energy performance data can better inform energy efficient policies for residential buildings, by highlighting the trend of energy consumption and in order to achieve the 40% carbon reduction by year 2020.
2.6 Summary
In summary, the building sector has tremendous potential to reduce GHG emissions by considering mitigation strategies such as low-energy building design, energy efficiency policies and building codes. Even with current voluntary measures such as the MS 1525:2007 and the GBI labelling programme, the existing residential typology is
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crucially excluded. The Malaysian government has yet to develop any national standard on energy efficiency for the building sector and specifically for residential buildings.
Many countries are adopting mandatory energy efficiency building codes, but Malaysia is a notable exception. This is concern considering the rate of economic development in Malaysia. A performance baseline is needed in order to monitor the building sector’s energy-related performance. Additionally, policy development requires MRV baseline data to determine the focus, scope and performance requirements. There are a range of existing methodologies for assessing GHG emissions from building energy use, employed in building rating tools and labelling schemes, but only the CCM has been designed to offer an internationally consistent MRV approach.
Moreover, the energy gap between predicted and measured building performance highlights the need to collect actual data during building operation to better inform simulation modelling tools with a feedback cycle of measured data to simulation models. Modelling tools that are used during the design stages mostly create simulations that are based on assumptions and generic building systems specification, instead of specific performance-based data and do take into account the impact occupants onto building systems. Therefore this research is significant in addressing this energy gap and informing policies through analysing purchased electricity bills that represent measured building energy performance of the Malaysian public low-cost housing typology, using the CCM.
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Chapter 3: Energy & Climate Change Lock-In Risks in the Malaysian Building Sector
3.1 Introduction
Countries in Asia and Africa are rapidly urbanizing, which has consequently caused a swift increase of GHG emissions, mainly from the housing sector, as a response to escalating housing demands (Fujita et al., 2009). In comparison with its other South East Asian (SEA) counterparts, Malaysia falls behind in implementing energy efficiency legislation for its building sector.
Without this, Malaysia is locking itself in to a predicted growth of GHG emissions from the building sector with greater energy consumption, as the country’s wealth and household income increases. Additionally, low-cost housing in Malaysia is defined by its sale price and does not include operational costs considerations. Therefore, operational costs of these low-cost housing projects should be investigated to ensure long-term affordability for low-income households. Consequently, this chapter will demonstrate the following issues:
i. Malaysia’s environmental and energy policy development; ii. The lack of energy efficiency legislation in the Malaysian building sector; and iii. Long-term operational affordability for low-cost housing.
Electricity is the predominant form of energy consumed in Malaysian buildings (90%), and approximately 51% of the total electricity generated is consumed by the building sector (Asia-Pacific Economic Cooperation (APEC), 2011b; Energy Commission, 2011b; Shafii, 2008; Zain-Ahmed, 2008b). This is further broken down to 20% for residential buildings, and 31% for commercial or non-residential buildings (between 2000 to 2010) (Energy Commission, 2011b; Shafii, 2008). Currently there is a lack of published data for energy performance and actual energy consumption by building typology, especially for residential buildings (Shafii, 2008). Annually the Malaysian building sector emits approximately 5,301 ktons of GHG emissions, with an annual growth rate of 6.4% (UNDP, 2009b).
Malaysia is classified as a tropical climate with average temperature of 24° to 32° Celsius and mean relative humidity of 81.6% (Department of Statistics, 2012), therefore
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Using an energy index43, the average energy consumption for a standard non-residential Malaysian building is between 250-300 kWh/m2/year (Shafii, 2008; Zain-Ahmed, 2008b). However, this rate is far from international best practice in energy efficient buildings. For example new office buildings in Germany were measured to consume approximately less than 100 kWh/m2/year (WBCSD, 2008). Similarly in the United Kingdom a typical non-residential energy efficient building is predicted to consume approximately 100 kWh/m2/year (Department for Communities and Local Government, 2007). Award winning energy efficient buildings in Singapore consume approximately between 138 to 174 kWh/m2/year (Eang & Priyadarsini, 2008)
Additionally, Singapore’s average non-residential building energy consumption is at 220 kWh/m2/year, while South East Asia’s region average is at 230 kWh/m2/year (Shafii, 2008; Zain-Ahmed, 2008b). The Malaysian building stock is approximately 38 million m2 floor area, of which 11% of the buildings can be considered as energy efficient (consuming less than 136 kWh/m2/year) (UNDP, 2009b). This clearly indicates that the Malaysian building sector is less energy efficient than its neighbours and there is a significant gap between Malaysian building energy efficiency and world’s best practice.
3.2 Building in the context of Environmental and Energy Policy Development in Malaysia
Malaysia is located in the South East Asian (SEA) region where most of its population and infrastructure is located along the coastline and river delta areas, putting hundreds of millions of people at high risk of climate change impacts (Srinivasan & Dobias, 2010).
43 total energy used in a building divided by gross floor area (Shafii, 2008; Zain-Ahmed, 2008b).
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Malaysia is also located within the important tropical rainforest belt ‘West Malesia’ with richly diverse flora and fauna and endowed with many natural resources (Hezri & Nordin Hasan, 2006; MNRE, 2011). Malaysia evolved from agriculture and mining in the colonial period between the late 19th to early 20th century, into manufacturing and industry in the mid 1990s (Hezri & Nordin Hasan, 2006).
Colonial expansion had caused rapid deforestation and loss of biodiversity due to agricultural land development and water pollution from mining (Hezri & Nordin Hasan, 2006; Hitam & Borhan, 2012). With the rise of manufacturing and industrialization, environmental challenges broadened to include waste and sewage management, toxic polluted river ecosystems, air pollution, and mineral resources sterilization (Afroz et al., 2003; Hezri & Nordin Hasan, 2006; Hitam & Borhan, 2012). Malaysia’s policy response to safeguard the environment while accommodating the demand for development can be categorized in four distinct stages starting from 1971 as:
1) Federal policy formulation (1971 to 1976); 2) Crisis and consolidation (1977 to 1988); 3) Sustainable development as a new concept (1989 to 2000); and 4) Implementing sustainable development (2001 to 2005) (Hezri & Nordin Hasan, 2006).
3.2.1 Malaysian Environmental Policy
The first stage of federal policy formulation was difficult to coordinate as constituent states had independence in matters pertaining to land, local government, and forestry (Hezri & Nordin Hasan, 2006). A National Forestry Council (NFC) was established in 1971 to facilitate nationwide policy development (Hezri & Nordin Hasan, 2006). The first environmental act was the enactment of Protection of Wildlife Act in 1972, which empowered the Department of Wildlife and National Parks (DWNP) to administer wildlife reserves on both public and private land (Hezri & Nordin Hasan, 2006).
The landmark Environmental Quality Act was later enacted in 1974 to provide a mandatory federal framework that focussed on pollution control (Hezri & Nordin Hasan, 2006; Mohammad, 2011). The Division of the Environment (DOE) was first established in 1975 under the Ministry of Local Government and Housing, but was later transferred
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The second stage of the environmental evolution in Malaysia followed as a series of crises of resource exploitation, and consolidation of policy (Ambali, 2011; Hezri & Nordin Hasan, 2006). A turning point that hallmarked environmental policy development was the dispute between Pahang State and the Federal Government over the issue of Endau-Rompin forest reserve (Ambali, 2011; Hezri & Nordin Hasan, 2006). The Pahang State government invoked its constitutional right to log the Endau-Rompin forest reserve, which produced a public protest (Hezri & Nordin Hasan, 2006). This placed pressure on the Federal Government to step in and endorse the National Forest Policy in 197745, which successfully stopped logging activities in 1979 (Ambali, 2011; Hezri & Nordin Hasan, 2006).
Other consolidations from growing pollution and environmental degradation during this period produced policies such as National Parks Act 1980, Environmental Quality Act 1974, and the Environmental Impact Assessment (EIA) requirement in 1987 (Hezri & Nordin Hasan, 2006). Under section 34A of the Environmental Quality Act, any “housing development covering an area of 50 hectares or more” (MNRE, 1987, p. 4) is required to produce and submit an EIA to the Department of Environment in MNRE (Hamzah, 2012).
The late 1980s to 2000 reflected Malaysia’s participation in the international discourse surrounding sustainable development, starting with the endorsement of the 1972 United Nations Conference on Human Environment (UNCHE) in Stockholm (Ambali, 2011; Hezri & Nordin Hasan, 2006). Malaysia also drafted the Langkawi Declaration on Environment and Development for the 46G-77 to introduce equitable responsibility and
44 “Policy directions in the Malaysian Federation are determined by the development planning mechanism. This four-tiered cascading horizon planning mechanism consists of the decadal outline perspective plan, the five-year development plan, mid-term review of the five-year plan, and the annual budget” (Hezri & Nordin Hasan, 2006, p. 40). 45 The National Forestry Policy 1977 was later enacted as the National Forestry Act in 1984 (Hezri & Hasan, 2006). 46 The Group of 77 or G-77 is “the largest intergovernmental organization of developing countries in the United Nations, which provides the means for the countries of the South to articulate and promote their collective
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During this period, Malaysia began to engage with foreign institutions for aid, such as the Danish Cooperation for Environment and Development (DANCED) to encourage strategic governmental intervention for policy development and environmental planning (Hezri & Nordin Hasan, 2006). Malaysia also became a part of many multilateral environmental agreements such as the Vienna Convention and the Montreal Protocol. The country was also recognized for its coordination efforts to phase out ozone-depleting substances with the 1995 Global Ozone Award from UNEP and the 1996 Stratospheric Ozone Protection Award from the United States Environment Protection Agency (USEPA) (Hezri & Nordin Hasan, 2006; United Nations, 2002).
However, Malaysia did not manage to localize the momentum of international sustainable development discourse in terms of institutional reforms (Hezri & Nordin Hasan, 2006). Sustainable development was symbolically recognized, but had no effect on policy reform for more practical outcomes of environmental awareness (Hezri & Nordin Hasan, 2006). Practical implementation and reform only began in the early 2000s, starting with the 8th Malaysia Plan (2001 to 2005) that incorporated environmental objectives into non-environmental sectors (Hezri & Nordin Hasan, 2006). Between this period, reforms were seen in water resource management that pushed for privatization of water services as a result of low quality water supply, interrupted services and low pressure (Hezri & Nordin Hasan, 2006).
During the 8th Malaysia Plan, the government gave substantial priority to integrating environmental concerns into planning, by encouraging more research and development in energy efficiency, forestry, waste and environmental management (Zainul Abidin, 2009). A National Water Resources Council was established to oversee inter-state water transfer and constitutional amendments were made that enabled the Federal Government to determine water tariffs (Hezri & Nordin Hasan, 2006; Zainal-Abidin,
economic interests and enhance their joint negotiating capacity on all major international economic issues within the United Nations system, and promote South-South cooperation for development” (Group of 77, 2012, Aims section, para. 2)
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2004). Similarly, solid waste management was also privatized and ‘federalized’ in the earl 2000’s (Hezri & Nordin Hasan, 2006).
In 2004, a cabinet reshuffle was introduced by the Ministry of Natural Resource and Environment (MNRE) under Prime Minister Abdullah Badawi, to reform the Ministry of Science, Technology and the Environment (MOSTE) (Hezri & Nordin Hasan, 2006). The NRE was set to combine all environmental portfolios such as forestry, wildlife, environmental conservation, marine parks, land surveying and mapping into one agency (Hezri & Nordin Hasan, 2006). The NRE is also the governing and coordinating authority responsible in addressing climate change issues in Malaysia (MNRE, 2011).
Going beyond 2005, Malaysia has seen more ministerial changes, for instance in 2009 a new Ministry of Energy, Green Technology and Water (Kementerian Tenaga, Teknology Hijau dan Air – KeTTHA) was established to replace the Ministry of Energy, Water and Communications (MEWC) (KeTTHA, 2009a). KeTTHA was introduced following a push for ‘green technology’ and as a “new initiative addressing global issues such as environmental pollution, ozone depletion, global warming and issues related thereto” (KeTTHA, 2009a). KeTTHA’s vision is to lead the industry and formulate policies on sustainable development of energy, water and green technology (KeTTHA, 2009a).
In 2009, the MNRE established the National Policy on Climate Change as a mobilizing framework for government agencies, industry and the community at large in tackling climate change issues (MNRE, 2009). In its policy statement, the ministry expressed a hope to “ensure climate-resilient development to fulfil national aspirations for sustainability” (MNRE, 2009, p. 1). However, this policy is yet to be enacted and therefore cannot be mandatorily enforced so its effectiveness remain untested. A brief list of environmental steps in terms of ministries established and policy development by means of statutes in Malaysia is presented as follows (Hezri & Nordin Hasan, 2006; Hitam & Borhan, 2012; Mohammad, 2011; MyCWP, 2011):
1) National Forestry Council (1971) 2) Protection of Wildlife Act (1972) 3) Environmental Quality Act (1974) 4) Division of the Environment (DOE) (1975) 5) Ministry of Science, Technology and Environment (MOSTE) (1976)
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6) National Forestry Policy 1977 7) National Parks Act 1980 8) National Forestry Act 1984 9) Environmental Impact Assessment (EIA) Order (1987) 10) National Policy on Biological Diversity (1998) 11) National Policy on the Environment (2002) 12) Ministry of Natural Resources and Environment (NRE) (2004) 13) Ministry of Energy, Green Technology and Water (KeTTHA) (2009) 14) National Green Technology Policy (2009) 15) National Climate Change Policy (2009)
Malaysia’s Second National Communication (NC2) to the UNFCCC produced by MNRE in 2011 presented a national GHG emissions reduction target of 26.79 million tonnes
(Mt CO2e.) of total net emissions, indicating a net sink47 in 2000 (MNRE, 2011). However, emissions from the energy, industrial and waste sectors had increased significantly from 1994. There was a 50% increase from the energy sector, 184% from industrial processes and an 80% increase from waste sector, from Malaysia’s Initial Communication to UNFCCC (INC) as shown in Figure 3.1 (MNRE, 2011). The only sector with a GHG reduction was the Land Use Change and Forestry sector (LULUCF), with a 260% decrease (also refer Figure 3.148) but it is largely due to the expansion49 of categories considered from the INC to NC2 (MNRE, 2011).
47 Net GHG removals by sinks can be defined as “The sum of the verifiable changes in carbon stocks in the carbon pools within a project boundary that are attributable to an afforestation and reforestation (A/R) or small-scale (SSC A/R) CDM project activity …” (UNFCCC, 2012, p. 4). 48 “Note: 1) Percentage indicates the increase or decrease of emissions or removals relative to the INC; and 2) With regards to the Waste Sector, INC is based on the recalculated value using the Revised 1996 IPCC Guidelines” (MNRE, 2011, p. 21). 49 Additional sub-sectors were included in the LULUCF sector for GHG emissions calculation in the NC2 that resulted in an increase in its overall total emissions contribution. The additional sub-sectors are “natural forest (permanent forest reserves, state land); urban forestry; bamboo and rattan; abandonment of managed land; and soil” (MNRE, 2011, p. 21).
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Figure 3.1 Comparison of GHG Emissions by Sector between INC and NC2 Source: MNRE (2011)
The energy sector is still the single largest contributing sector to Malaysia’s GHG emissions, contributing 66% in 2000 (MNRE, 2011). Additionally, business-as-usual
(BAU) projections for the energy sector are expected to produce 259.8 Mt CO2 by 2020, but successful mitigation50 would reduce emissions to 234.1 Mt CO2 by 2020 (MNRE, 2011). Consequently, Malaysia’s energy policy development is presented next.
3.2.2 Development of Energy Policy in Malaysia
Malaysia’s energy policy began in the mid 1970s with its National Petroleum Policy in 1975, followed by the National Energy Policy in 1979 (Chua & Oh, 2010). Following an energy crisis, the National Depletion Policy and a Four Fuel Diversification Policy was established in 1979 and 1981 respectively, to address energy security issues and the exploitation of oil and gas reserves (Chua & Oh, 2010; Hashim & Ho, 2011). A brief energy policy development chronology is presented in Table 3.1.
50 Mitigation activities included in the projection are from renewable energy, and energy efficiency through the Malaysian Industrial Energy Efficiency Improvement Programme (MIEEIP) (MNRE, 2011).
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Policy/Act Key focus
1 National Petroleum Policy Optimal consumption of petroleum resources; (1975) Regulation of ownership; Environmental safeguard against exploitation;
2 National Energy Policy Long-term energy objectives for efficiency, security and minimizing (1979) environmental impacts i.e. supply objective, utilization objective and environment objective.
3 National Depletion Policy Safeguard against exploitation of oil and gas reverses. (1980) 4 Four Fuel Diversification Fuel diversification of energy supply from oil, gas, hydro, and coal.; Policy (1981) Divert over-dependence of oil as primary energy supply.
5 Electricity Supply Act (1990) Regulate licensing of electricity generation, transmission and distribution.
6 Gas Supply Act (1993) Regulate licensing of gas supplier; Control and safety of supply pipelines.
7 Fifth Fuel Policy (2000) Introduced renewable energy resource for electricity generation from biomass, biogas, municipal waste, solar and mini hydro. 8 Energy Commission Act Establishment of Energy Commission to regulate performance of (2001) electricity and piped gas supply industries; Electricity Supply Act and Gas Supply Act amended to hand over responsibility.
9 National Bio-fuel Policy Aimed at support the Fifth Fuel Policy and promoting palm oil as an (2006) alternative to reduce dependence on depleting fossil fuels.
10 National Green Technology Reduce energy consumption rate; Policy (2009) Facilitate growth of green technology industry to enhance national economy contribution; Ensure sustainable development and environmental conservation.
11 Renewable Energy Act Establishing and implementing special tariff system for renewable (2011) energy generation
12 Sustainable Energy Establishment of the Sustainable Energy Development Authority of Development Authority Act Malaysia (SEDA) to implement, manage and monitor the special tariff (2011) system; Advise Minister/s and government entities pertaining sustainable energy, including policy and legislation recommendations.
Table 3.1 Energy Policy Development in Malaysia Source: (Chua & Oh, 2010; Hashim & Ho, 2011; Maulud & Saidi, 2012; SEDA, 2012)
Progressively, Malaysia shifted its focus from fossil fuels to renewable energy beginning with the Fifth Fuel Policy in 2000 (Chua & Oh, 2010; Hashim & Ho, 2011). Later, more emphasis was given to the development and utilization of renewable energy (RE), and the sustainable consumption of all energy resources (Chua & Oh, 2010). During the same period (early 2000s), energy efficiency (EE) in the industrial, commercial and
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Malaysia together with the Global Environment Facility and United National Development Programme (GEF-UNDP) launched the Malaysian Building Integrated Photovoltaic Technology Application (MBIPV) in 2005 (GEF, 2004; Hashim & Ho, 2011). The MBIPV was a 5 year project funded by GEF-UNDP, aimed at reducing GHG emissions from the building sector by reducing the long-term costs of photovoltaic (PV) and mainstreaming PV into the Malaysian market (GEF, 2004; Hashim & Ho, 2011). The completed project was deemed successful in catalysing a local PV market, which also saw the formulation of a Malaysian Standard (MS 1837:2005) – Installation of Grid- Connected Photovoltaic Systems in 2005 (UNDP Malaysia, 2010). Additionally, the project was successful in exceeding its target of a 330% increase in building integrated photovoltaic (BIPV) capacity against baseline to a 539% increase by 2010 (UNDP
Malaysia, 2010). This consequently reduced GHG emissions by 1070 tonnes of CO2 equivalent, through installation of BIPV systems (Hashim & Ho, 2011).
CETREE was established as a continuation of the Danish-Malaysian environmental cooperation programme (DANIDA), for the purpose of research and development in energy efficiency and conservation. CETREE was placed within the University of Science, Malaysia (KeTTHA, 2009b; Komori et al., 2012). CETREE’s main aim is to “increase the level of knowledge and awareness of the role and use of energy efficiency in education” (KeTTHA, 2009b, para. 1). In 2006, together with the Ministry of Education, CETREE organized three national competitions that incorporated RE and EE concepts such for beach houses, solar cars and cooking appliances (KeTTHA, 2009b). Despite these policy developments for energy efficiency and conservation, an integrated implementation and operational framework for the building sector is yet to be formed. Therefore, adopting the UNEP-SCBI’s Common Carbon Metric to investigate operational performance of the Malaysian building sector is suggested.
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3.3 A Project-Specific Baseline using UNEP-SBCI’s CCM
This section presents the key parameters for a project-specific baseline in this research context. Adopting a project-specific case study methodology will allow similar research to be conducted for other building typologies within the residential sector. Selecting a suitable case study is essential in providing a naturalistic generalization of the phenomenon researched (Stake, 1995; Yin, 2003). This section lists the key elements investigated in this research and the justification for each element.
3.3.1 Electricity Consumption as Primary Indicator of GHG Emission
The World Bank51 estimates Malaysia’s annual CO2 emissions at 7.6 million tonnes of carbon dioxide (MtCO2) per capita, equivalent to 7,600 kilogram of carbon dioxide
(kgCO2) per capita for year 2008 (World Bank, 2012). Malaysia’s GHG emissions are higher in comparison with developing countries like China and India, with GHG emissions of 5.3 MtCO2 per capita and 1.5 MtCO2 per capita, respectively (World Bank, 2012). As described in Chapter 1, electricity is the single largest contributing sector (representing approximately 43% of national emissions annually) to Malaysia’s alarming GHG emission growth (EPU, 2006; Safaai et al., 2010; Zain-Ahmed, 2008b).
Based on Malaysia’s Second National Communications to the UNFCCC, Malaysia’s overall national energy demand is estimated to grow at 4.8% annually52 from 2000 to 2020, while the combined residential and commercial building sectors are expected to grow at 5.6% annually (MNRE, 2011). This translates an increase of CO2 emissions at a rate of 3.7% annually from 2000 to 2020, with the present estimate of 180,716 giga-gram of carbon dioxide (GgCO2) for 2010 (MNRE, 2011). Similar to Malaysia’s expected increase in energy demand, electricity consumption in Malaysia is also anticipated to grow at a rate of 5.7% annually (MNRE, 2011).
51 The World Bank presents a database for national CO2 emissions in metric tons per capita (World Bank, 2012). The CO2 emissions are defined by emissions “stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring” (World Bank, 2012). However, this database does not present disaggregated CO2 emissions for the contributing sectors such as industry, transportation, residential and commercial buildings. 52 The estimations are based on a business-as-usual (BAU) scenario (Ministry of Natural Resources and Enviroment, 2011)
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In 2010, electricity consumption was estimated at 104,519 Gigawatt hour (GWh) (Energy Commission, 2011b) and approximately 3,746 kWh per capita (Energy Commission, 2010). According to the 2010 National Energy Balance, Malaysia’s electricity consumption has seen a steady increase annually as the country’s gross domestic product (GDP) increases (refer Figure 3.2) (Energy Commission, 2011b). The existing trend clearly indicates that Malaysia’s electricity consumption increases as income grows, which supports the expected growth of energy demand and GHG emissions by 2020.
Between 2000 and 2010, the Energy Commission of Malaysia recorded that approximately 20% of Malaysia’s national electricity is being consumed by the residential sector (Energy Commission, 2011b). This is an exponential increase of 50% in just the last decade, from 975 kilo tonnes of oil equivalent (ktoe) to 1937 ktoe between 2000 to 2010 (refer Appendix 3.3) (Energy Commission, 2011b). It is estimated that the average Malaysian household53 electricity consumption is 251 kWh/month, and emits approximately 172 kgCO2/month for 2010 (Noordin, 2012). This provides an estimated electricity consumption of 3,012 kWh/household/year, and GHG emissions of 2,064 kgCO2/household/year. More significantly, this suggests that Malaysia’s average household consumes six (6) times more than the world average of 500 kWh/household/year under the International Energy Agency (IEA) survey (IEA, 2011d).
At present, disaggregated energy performance data by building typology is not available, which subsequently hinders the development of baselines projection that is needed for GHG mitigation policy development. The national electricity consumption data made available by the Energy Commission of Malaysia is aggregated by sector (Energy Commission, 2011b), but not by building typology. However, an energy audit study in 2002 by the Malaysian Energy Centre (known as Pusat Tenaga Malaysia) of twelve (12) government buildings, that included residential and non-residential buildings, was conducted as an effort to encourage energy efficiency (New Energy and Industrial Technology Development Organization (NEDO), 2004).
53 It must be noted that the number of persons per household was not mentioned this report. However, the report cited data from the 2010 Population and Housing Census. According to the Census, the Malaysian average household size is 4.31 persons (Department of Statistic Malaysia, 2011).
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Figure 3.2 Trends in GDP and Electricity Consumption Source: (Department of Statistics, 2011b; KeTTHA, 2009c) cited in (Energy Commission, 2011b).
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The average energy consumption of those 12 government buildings was calculated at 180.41 kWh/m2/year (NEDO, 2004). This is well above the recommended MS 1525:2007 of 135 kWh/m2/year (SIRIM, 2007) and indicates a need for energy efficiency and conservation policies, currently absent in the Malaysian building sector. A key recommendation of the energy audit study was that more detailed and continuous surveys be extended to other building typologies and that the number of buildings surveyed be increased in order to develop a database to set benchmarks and guidelines (NEDO, 2004).
The report also recommended that energy consumption be calculated in terms of 24-hour measurements and by end-use consumption, to determine points for energy conservation improvements (NEDO, 2004). In keeping with the above recommendations, this research has focused on end-use electricity consumption based on 24-hour measurement to investigate the pattern of energy consumption for the low-cost housing typology.
3.3.2 Baseline End-Use Energy use of Malaysian Households
As previously discussed, this research addresses this lack of environmental research within the low-cost housing typology in Malaysia in general, and on investigating the end-use electricity consumption of public low-cost housing. The parameters for determining baseline electricity consumption in low-cost housing project therefore need to be identified.
Based on a report by the Malaysian national energy provider company, Tenaga Nasional Berhad (TNB) in 1999, Malaysia’s average household energy consumption is approximately 2,754 kWh/household/year (Tang, 2005). A follow-up research was conducted in 2005 on urban households across the country, and on several building typologies such as bungalows, single and double storey terrace houses, and apartments (Aun, 2009; Tang, 2005). The average household electricity consumption measured to be approximately 2200 kWh/yr/household, for 2005 (refer Table 3.2) (Tang, 2005). In comparison to IEA’s estimation of 550 kWh/household/year, Malaysia’s average household energy consumption is more than five times the world average. However, considering the different methodologies and size of household adopted for these reports, comparison may be misleading.
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Tang (2005) predicted that the Malaysian average household electricity consumption will increase to approximately 3,707 kWh/year for 2010 and 6,714 kWh/year for 2020, based on household income and appliance ownership (Tang, 2005). However, according to Noordin’s (2012) estimation, the average Malaysian household consumed approximately 3,012 kWh/household/year of electricity in 2011 (Noordin, 2012). In comparison, Noordin’s (2012) data seems to suggest that the average Malaysian household electricity consumption for 2011 was lower than the consumption predicted by Tang (2005) for 2010. Additionally, Noordin’s (2012) estimation suggested that the average Malaysian household electricity consumption is below the WEC’s estimated world average household energy consumption of 3,500 kWh/household/year. These prediction and data can be used as a benchmark for the research findings.
Tang’s (2005) report can also be used to compare average operating time utilized for electrical appliances, in efforts to investigate household end-use electricity consumption patterns. Another research that investigated end-use energy patterns from household appliances in Malaysia was conducted by Saidur, et al. (2007) (refer Appendix 3.4). Both research findings are presented in Table 3.3 categorized by type of electrical appliance.
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Table 3.2 Future Scenarios of Household Electricity Consumption Source: Tang (2005)
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Tang (2005) Saidur, et al. (2007) Types of Electrical (Average Operating (Average Operating Appliance Time Daily) Time Daily)
Artificial Lighting 4 8.98
Artificial Cooling 11.3 16.7
Hot Water System 1.0 0.7
Refrigeration 24 8.11 Entertainment & 2.9 17.56 Technology Cooking & Kitchen 1.0 1.75 Ware Clothes Washing 0.8 1.04
Table 3.3 Average Operating Time of Electrical Appliances Source: (Saidur et al., 2007; Tang, 2005).
This research has identified seven types of electrical appliances and equipment that were aggregated into and modified in reference to previous research investigating household energy consumption by the Centre for Environment, Technology and Development Malaysia (CETDEM) in 2004, and by the Malaysian-Danish Environmental Cooperation Programme under Danish International Development Assistance (DANIDA) in 2005 (an extension to the CETDEM research) (Aun, 2009; Tang, 2005). Similarly, Singapore’s National Environment Agency (NEA) study on household electricity consumption also included similar electrical appliances, such as air-conditioners, refrigerators, lighting, water heaters, fans, video equipment, kitchen appliances, and washing machines (NEA, 2008).
3.4 Climate Change and Affordability
This research specifically focuses on the National Economic Action Council’s (NEAC) People’s Housing Programme– Program Perumahan Rakyat (PPR) housing projects as the national standard of public low-cost housing projects. This section discusses issues related to climate change and affordability, such as energy poverty and rebound effect. This section also discusses housing affordability in a global context and prescribes the definition of public low-cost housing in Malaysia and its social, economical and environmental implications.
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3.4.1 Affordability, Energy Poverty and Rebound Effect
Albeit improved energy efficiency in buildings, it has yet lead to significant emissions reductions, and at best is only able to compensate for the increase in service demand (Urge-Vorsatz et al., 2012a). This is because energy efficiency by definition is “simply the ratio of energy services out to energy input” (Herring, 2006 p.11). In other words, it is a process to maximize every unit of energy bought, which does not necessarily imply energy reduction or consequently GHG emissions reduction (Herring, 2006).
Moreover, some argue by improving efficiency of energy production, it reduces energy prices that potentially leads to greater consumption (Herring, 2006). This phenomenon is described as the ‘rebound effect’ (Koeppel & Urge-Vorstaz, 2007; Levine et al., 2007; World Business Council for Sustainable Development (WBCSD), 2008). The rebound effect is often cited as one major barrier to the implementation of energy efficiency policies (Levine et al., 2007). The rebound effect can be identified into three categories, i.e. the direct rebound effect, the indirect rebound effect, and the general equilibrium (or economy wide rebound) effect (Herring, 2006; Maxwell et al., 2011; WBCSD, 2008).
The direct rebound effect is the increased consumption of energy services induced by reduction in price from increased efficiency (Herring, 2006; Maxwell et al., 2011; WBCSD, 2008). Indirect rebound effect occurs when the price reduction of energy services encourages the consumer to spend on other goods and services, which consequently increases energy consumption from the added goods and services (Herring, 2006; Maxwell et al., 2011; WBCSD, 2008). The general equilibrium or economy wide effect transpires when efficiency drives economic productivity resulting an increase in demand and consumption at a macroeconomic level in all sectors which has the same ultimate effect that indirect rebound has (Herring, 2006; Maxwell et al., 2011; WBCSD, 2008).
Equally, while many developed nations have progressed to considering energy security issues or decarbonising their energy mix, affordability and reliability issues are the main focus of many developing countries (IEA, 2011d). Many developing countries are more concerned about energy poverty than providing affordable access to energy services, “in order to support economic and social development of the society, the business sector, and individuals” (Winkler et al., 2011 p.1037). Affordable access to
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Affordability of electricity is also important due to issues such as energy poverty, access to energy and the rebound effect (Fox, 2012; Herring, 2006). A household’s inability to afford energy services has also been argued as a form of social inequality and injustice (Walker & Day, 2012). The inability to afford sufficient heating can lead to health issues such as respiratory, circulatory problems and risk of hypothermia (Hills, 2011). Particularly, in the case of seasonal climates, there is substantive evidence that relate health issues to living in low temperatures, for example, due to fuel poverty (Hills, 2011). Two policy recommendations in relation to affordability of energy, focusing on low-income households, were made by (Heltberg, 2003) and are listed as the following:
1) Subsidies needs to be better targeted for poor consumers or households, and fiscal support should be directed for grid expansions and fuel uptake; and 2) Creating an even level energy playing field, whereby households are able to choose its fuel type in order to reduce energy bills as energy price fluctuates.
Energy poverty can be defined either by inadequate access to energy services, or the inability to afford energy services (Fankhauser & Tepic, 2005; Tirado Herrero & Ürge- Vorsatz, 2011). The International Energy Agency’s World Energy Outlook 2011 defined modern energy access as “a household having reliable and affordable access to clean cooking facilities, a first connections to electricity and then an increasing level of electricity consumption over time to reach the regional average” (IEA, 2011d p. 12). It is estimated over 1.3 billion people do not have access to electricity, and approximately 95% are located in Sub-Saharan Africa or developing Asia, and 85% of them are in rural areas (IEA, 2011d).
However, access to electricity isn’t an appropriate benchmark for energy poverty in the Malaysian context, since the electrification rate54 is 99.4% of its total population (IEA, 2011b; World Bank, 2012). Therefore, energy poverty in this research refers to the inability to afford energy services.
54 Electrification rate can be defined as “the share of population with access to electricity” (IEA, 2011d p.10)
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3.4.1 Housing Affordability
Investigating the percentage of monthly household income spent on electricity, which is the main energy source consumed in buildings, will help measure operational and long term affordability of public low-cost housing. Therefore, this research’s contribution to knowledge is investigating operational costs and its long-term affordability for low- income households.
According to Bujang (2006) and Zebardast (2006), housing affordability can be defined by the ratio of household income to the monthly housing loan payment or rent, which is less than 30% of its monthly income (Bujang, 2006; Zebardast, 2006). Conversely, according to the international benchmark for housing affordability, average housing expenditure can be further disaggregated to electricity (10%), heating (12%), and water (3.6%) (Fankhauser & Tepic, 2007). Smith’s (2009) research on home ownership affordability divided housing costs into four categories, as:
1) Capital/acquisition costs – Purchase price and ‘up-front’ costs; 2) Pre-purchase costs – Conveyance/legal fees, stamp duty, inspection fees, council/water rates, etc; 3) Finance costs – Establishment fees, mortgage insurance, mortgage repayments; 4) Operational costs – Water charges, service charges, insurances, intermittent costs for maintenance, repairs, renovations, etc (Smith, 2009).
According to the Central Bank of Malaysia, in 2010 households spend approximately 19% of household income for the combination of “housing, water, electricity, gas and fuels” (Central Bank of Malaysia, 2010 p.20) (refer Figure 3.3). Similarly, the average monthly household expenditure published by Department of Statistics Malaysia also combined expenses for housing, water, electricity, gas and other fuels, which calculated an increase of expenditure by 15.1% for 2010 from 2005 (Department of Statistics, 2010).
Other high percentages of household expenditure include food and non-alcoholic beverages (23%), transport (13.4%), and miscellaneous goods and services (12.8%) (also refer Figure 3.3). (Central Bank of Malaysia, 2010). Other utility expenses such as telephone and internet should also be included while determining affordability to keep up with current demands and changing housing needs (Litman, 2013; Smith, 2010). An
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% of Household Consumption Food and non-alcoholic beverages
Alcoholic, beverages and tobacco 13% 23% Clothing and footware 7% Housing, water, electricity, gas and fuels 2% 2% 5% Furnishing, household equipment and 3% maintenance 6% Health
Transport 13% 19% Communication 2% 5% Recreation and culture
Figure 3.3 Average Malaysian Household Consumption, Based on Monthly Income Source: Central Bank of Malaysia (2010)
A compilation of other international data on household expenditure for utilities to percentage of income is presented in Table 3.4. Data was collected from multiple sources to calculate an approximate average of household income percentage spent on different utilities such as fuels, water and other utilities. The tabulated data presented an approximate average of 10% spent on fuels, and 6% for the combined utilities from total household income (Australian Bureau of Statistics, 2011; Central Bank of Malaysia, 2010; Department of Statistics, 2011a; Department of Statistics Singapore, 2008; Fankhauser & Tepic, 2007; ILO, 2010; Ministry for the Environment, 2009).
This operational cost should be an important consideration in defining housing affordability of low-cost housing projects in Malaysia, which is not inscribed in the current definition. Hence, definition of operational affordability for housing in this research will consist of the percentage of monthly housing loan payment/rent and the operational costs of electricity and other utilities, defined in terms of percentage of household expenditure to monthly income, i.e.:
less than 30% for rent/housing loan repayment; less than 10% for electricity; and less than 6% for other utilities (including water, telephone, internet, etc).
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Fuel Other Utilities % of Average Household Income (Electricity and other Water Utility (including telephone, fuels) postal, internet, etc) International Labour Organization 3.4% 6.6% 3.3% (ILO)55 (2010) (communication only)
World Bank (2004) 10-15% 3 - 5% - (from Fankhauser & Tepic, 2007
World Health Organization (WHO) 10% - - (2004) (from Fankhauser & Tepic, 2007
IPA Energy (2003) 10% - (from Fankhauser & Tepic, 2007 United Kingdom Government - 3% - (from Fankhauser & Tepic, 2007 United States Government - 2.5% - (from Fankhauser & Tepic, 2007
Asian Development Bank (2003) - 5% - (from Fankhauser & Tepic, 2007 5.6% Malaysia (2010) 19% (housing, water, electricity) (communication only) 3.3 % Australia (2009) 28.2% (housing, water, electricity) (communication only) 11.1% New Zealand (2009) 13.1% (telephone, water, etc) 4.8% Singapore (2008) 2.8% 2.8%56 (communication only)
Total % 57.5% 49.8%
Average % 9.6%57 5.5%
Table 3.4 Benchmarks used in measuring affordability (% of total household income/expenditure) Source: (Australian Bureau of Statistics, 2011; Central Bank of Malaysia, 2010; Department of Statistics, 2011a; Department of Statistics Singapore, 2008; Fankhauser & Tepic, 2007; ILO, 2010; Ministry for the Environment, 2009).
55 Countries included International Labour Organization Household Income and Expenditure Statistics are ALBANIA; ANDORRA; ARGENTINA; ARMENIA; AUSTRALIA; AUSTRIA; AZERBAIJAN; BELARUS; BELGIUM; BOTSWANA; BULGARIA; CROATIA; CYPRUS; CZECH REPUBLIC; DENMARK; ESTONIA; FINLAND; FRANCE; GERMANY; GIBRALTAR; HONG KONG, CHINA; HUNGARY; ICELAND; INDIA; IRAN, ISLAMIC REP. OF; ISLE OF MAN; JAPAN; KAZAKHSTAN; KOREA, REPUBLIC OF; LATVIA; LITHUANIA; MACAU, CHINA; MALDIVES; MAURITIUS; MEXICO; MOLDOVA, REPUBLIC OF.; MYANMAR; NETHERLANDS; NIGER; NORWAY; PANAMA; PHILIPPINES; POLAND; ROMANIA; SERBIA AND MONTENEGRO; SINGAPORE; SLOVAKIA; SPAIN; SRI LANKA; SWEDEN; SWITZERLAND; TURKEY; UGANDA; UNITED KINGDOM; UNITED STATES; WEST BANK AND GAZA STRIP (ILO, 2010). 56 This percentage includes “water supply and miscellaneous services relating to the dwelling” (Department of Statistics Singapore, 2008, p. 6) 57 The Total and Average calculations are based on the highest percentage cited and averaged only for ‘Fuel’ ‘Other Utilities’ which includes water utilities.
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3.4.2 Affordability of Electricity for Malaysian Low-Income Households
Affordability of electricity has be defined as “whether households can afford to actually use electricity once they are connected to the grid58” (Winkler et al., 2011, p. 1038). Winkler, et al. (2011) measured affordability of electricity in relation to household income, purchasing power and the relative price of electricity in comparison to other commodities. Similarly as presented in Chapter 3, the international benchmark for affordability of electricity is measured to be less than 10% of household monthly income (Australian Bureau of Statistics, 2011; Department of Statistics, 2011a; Fankhauser & Tepic, 2007; ILO, 2010; Ministry for the Environment, 2009).
Comparing international standards on household income and expenditure for electricity will help gauge the effectiveness of a publicly funded low-cost housing project for lower income households in Malaysia. The 1999 TNB report indicated that electricity consumption increased according to household income (Hasan, 2011; Tang, 2005), where the trend of increase is almost linear and as a direct correlation with income (refer Figure 3.4). A similar trend can also be seen in the national electricity consumption and GDP graph shown earlier (Energy Commission, 2011b), which correlates the increase in consumption as Malaysia’s GDP increases.
Figure 3.4 Average Annual Energy Consumption by Household Income Level Source: TNB (1999) cited in Hasan (2011)
58 Grid in this context means access to national grid electricity (Winkler et al., 2011).
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Table 3.5 presents average Malaysian household energy consumption in kilowatts per year (kWh/year) based on monthly income level, for 1999 (Tang, 2005). Comparing the data with the low-cost housing definition in this research (i.e. households that earn less than RM 4,000 a month for urban areas) (City Hall of Kuala Lumpur, 2009a; National Housing Department, 2008), the average low-cost housing household energy consumption would be approximately 2,754 kWh/year/household59 (also refer Table 3.5) (Tang, 2005). This will be the benchmark adopted in this research. Investigating the affordability of electricity for low-income households can also indicate if an energy poverty scenario exists, in comparison to the international electricity affordability standard which less than 10% of monthly household income (Fankhauser & Tepic, 2007). Table 3.6 indicates the exchange rate between the Malaysian currency (Ringgit Malaysia) to US Dollar and Australia Dollar (XE, 2013).
Household Income Level (RM/month)
Below Above 5000 Above 4000 Above 3000 Above 2000 Above 1000 1000
Average Energy Used per Household 3268 2754 2495 2130 1611 1409 (kWh/year)
Table 3.5 Average Annual Energy Consumption by Household Income Level by TNB Source: TNB (1999) cited in Tang (2005)
Exchange Rate as of 18th October 2013 (XE, 2013). Ringgit Malaysia US Dollar Australia Dollar (RM) (USD) (AUD)
5000 1,584 1,640
4000 1,267 1,312
3000 950 984
2000 634 656
1000 317 328
Table 3.6 Exchange Rate Table from Ringgit Malaysia to US Dollar and Australia Dollar Source: (XE, 2013).
59 However, the average number of occupants per household was not presented in this report. Therefore, any comparison made against this benchmark acknowledges that it is only an approximation.
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According to the New Economic Model set by the Performance Management and Delivery Unit (PEMANDU) under the Prime Minister’s Department, the income level of households is expected to rise 6% annually as Malaysia gears towards becoming a fully developed and high-income nation60 by 2020 (PEMANDU, 2010). As the level of household income increases, Tang (2005) predicts that electricity consumption will also increase. In other words, Malaysia’s electricity demand will increase as the country’s economy grows. While the calculation of rebound effect is not the focus of this study, an investigation of household electricity consumption based on household income provides a baseline that may support future research on the subject.
3.5 Summary
Therefore in summary, there is an absence of mandatory energy efficiency legislation for the Malaysian building sector, which consequently puts the country at high risk for carbon lock-in. The increasing awareness globally of the environmental footprint by the building sector has produced various environmental building assessment tools and international standards to guide the industry in reducing its impact. Although some level of awareness is evident in the Malaysian context, the building sector still falls behind in addressing its environmental impact. This chapter justified using a project- specific baseline, described its key parameters, in measuring electricity related consumption and emissions, as electricity in Malaysia is the main contributing factor in building-related GHG emissions
There is also an apparent lack of environmental perspective in low-cost housing developments in Malaysia. Therefore, measuring the GHG emissions of the existing public low-cost housing building stock can be used as a benchmark for future low-cost housing developments for both the public and private sectors. Generating a baseline for operational energy-related GHG emission in low-cost housing will help develop a more environmental friendly building operation, and improve energy efficient design and policy development for the residential sector. Providing more environmentally friendly homes are one of objectives in the new National Housing Policy.
Aside from the lack of building energy performance database, disaggregated data on operational cost of household expenditure for rent, housing loan repayment, electricity
60 The high income definition as per capita income of USD 15,000 (or RM 46,395, based on exchange rate 15th February 2013) (XE, 2013) is adopted from the World Bank’s definition of high income (PEMANDU, 2010).
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Chapter 4: Research Design: Case-Study, Baselines & Systems Boundary
4.1 Introduction
Preceding chapters defined the need for building energy efficiency regulation and the rationale for measuring and verifying baselines for electricity consumption and performance in Malaysian low-cost housing. Chapter 4 will illustrate the need for a survey questionnaire to investigate the end-use electricity consumption patterns of low- income households and its affordability. This chapter also describes the analytical framework used in collating the required data and its sources of information, in reaching the research objectives.
The CCM analyses purchased electricity bills to measure the operational GHG emission, and the survey questionnaire responses provide end-use pattern of electricity consumption and the affordability of electricity to the end-users. A case study methodology has been adopted to provide a project-specific baseline, employing both top- down and bottom-up approaches. A case study method is also the most appropriate in determining a project-specific baseline because it provides a generalized description of a bounded system (Aziz, 2007). A project-specific baseline is appropriate because:
- this research focuses on a specific system boundary of indirect GHG emission from building operation, which is the case with public low-cost housing projects in Kuala Lumpur; and - the availability of multiple sources of data, in terms of electricity bills and survey questionnaire.
The project-specific baseline definition adopted in this research is a baseline that is determined by project-specific measurements or assumptions for its key considerations (Ellis & Bosi, 2000; Gustavsson et al., 2000). The key consideration in this research is end-use electricity consumption in a case study of public low-cost housing projects in Kuala Lumpur, in both determining its GHG emissions and affordability.
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4.2 Analytical Framework
The analytical framework organizes the criteria used to analyse the survey questionnaire responses and results of the application of the CCM to the case-study projects. The research design and methodology used in this study are a first step in the development of a national GHG emission baseline methodology for the low-cost housing typology, which can inform the development of an energy efficiency policy for the residential sector.
The analytical framework explains how the selected PPR low-cost housing units were analysed in three sets of analysis, using two methods of investigation, i.e. the UNEP- SBCI Common Carbon Metric and a survey questionnaire (refer Figure 4.1):
1) Indirect GHG emission from building operation (using CCM); 2) Pattern of electricity consumption (using survey questionnaire); and 3) Affordability of electricity (using survey questionnaire).
The analytical framework is constructed in such a way to reflect a top-down and bottom- up approach. The top-down approach is mainly reflected via the CCM, while the bottom- up approach is adopted for both the CCM and the survey questionnaire. The analytical framework also presents the data sources for each of the steps required in both the CCM and survey questionnaire, mainly located on the furthest right and left sides of the analytical framework.
The bottom-up approach applied via the CCM is on a building or case-study scale, in which CCM converts electricity bills to indirect GHG emission of selected PPR low-cost housing projects. The survey questionnaire also is done on a case study scale, based on a representative sample size of households living in the two selected PPR low-cost housing projects, i.e. PPR Beringin and PPR Intan Baiduri. The data collected using the survey and CCM can be used to build a national baseline by multiplying the bottom-up case study data with national percentage or average.
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The top-down approach is reflected by disaggregating energy data from the national energy balance to the energy consumed by the building stock. The CCM’s top-down approach analyses the building stock’s energy consumption during operations and related GHG emissions. The data collection protocol and analysis of both top-down and bottom-up approaches of the CCM is further discussed in Chapter 5 and Chapter 6 respectively.
Figure 4.1 Top-Down and Bottom-Up Analytical Framework
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After establishing the systems boundary and analytical framework, two pilot case studies were conducting in order to test the research methodology and accessibility of data. Conducting the pilot studies also helped determine the most appropriate case study protocol, in collecting the relevant data.
4.2.1 Piloting the Case Study
The pilot case study was conducted in two phases; from December 2009 to February 2010 (‘Pilot Study Phase 1’), and from November 2010 to December 2010 (‘Pilot Study Phase 2’). The main objective of the pilot study was to determine the availability of data according to the predetermined systems boundary and accessibility to sources according to the analytical framework, namely the City Hall of Kuala Lumpur and Tenaga Nasional Berhad. ‘Pilot Study Phase 2’ had the additional objective of testing the UNEP- SBCI’s Common Carbon Metric in three low-cost housing projects (also as part of UNEP- SBCI’s Phase 2 pilot testing project). Data collected for the pilot study was presented at the 2011 World Sustainable Building Conference (SB) in Helsinki. (See Zaid & Graham, 2011). A summary of the two pilot studies is set out below:
Pilot Study Phase 1 Date: December 2009 to February 2010 (approximately 13 weeks). Objective: Identify sources and potential case study. Task: a) Interview stakeholders from government bodies (Economic Planning Unit, City Hall of Kuala Lumpur, National Housing Department) and academicians. Outcome: a) Access to data is limited. Special authorization from City Hall of Kuala Lumpur is required in order to conduct investigation on PPR housing projects in Kuala Lumpur; b) Need to engage with national electricity provider company (TNB) to access household electricity consumption data.
Pilot Study Phase 2 Date: November 2010 to December 2010 (approximately 6 weeks). Objective: Test the UNEP-SBCI’s Common Carbon Metric.
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Task: a) Request permission to conduct case study investigation from City Hall of Kuala Lumpur; b) Engage with TNB to identify contact person in providing household electricity bills.
Outcome: a) Conduct pilot field work investigation: two public PPR low-cost housing projects (PPR Intan Baiduri and PPR Beringin), and one private low-cost housing complex (Mutiara Fadason); b) Test CCM to calculate GHG emissions of nine (9) household units in the three low-cost housing projects (three household units per low-cost housing project); c) Data collected was presented at SB 11 conference in Helsinki, and collated to UNEP-SBCI for their Phase 2 pilot testing project.
4.3 Adopting a Survey Questionnaire
This section explains the need for a survey questionnaire to address the second and third aims of this research, which are to investigate the end-use electricity consumption of households and measure operational affordability of PPR public low-cost housing projects. A survey questionnaire is used to quantify ‘something’ within a defined population, or used to test hypothesis and models in fields like economics, sociology, and psychology (Czaja & Blair, 2005; Farrell, 2011). A survey questionnaire is a scientific method of gathering accurate and useful information, as it depicts probability or random samples from a larger and pre-determined population (Salant & Dillman, 1994). A population can consist of individuals or elements, such as persons, events, cities, patients, hospitals, etc (Schofield, 1996). A sample is a set of individuals or elements, that are selected in some way from a population (Schofield, 1996). Data collected from the sample size is an estimate of the population based on the specified parameters (Bluman, 2012).
In order to investigate patterns of household energy consumption and expenditure surveys of households are commonly employed. For example, surveys have been used to investigate the affordability of electricity (see Heltberg, 2003; Platchkov & Pollitt, 2011; Winkler et al., 2011) and end-use energy consumption patterns for GHG emissions reduction (Girod & de Haan, 2009; see Streimikiene & Volochovic, 2011). Household 126 Chapter 4: Research Design: Case-Study, Baselines & Systems Boundary
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A survey questionnaire can be useful to determine the affordability of electricity for a sample of low-income households in PPR public low-cost housing projects. It can also reveal the end-use electricity consumption patterns of the average household. This will provide an understanding to how occupants consume electricity based on four different categories of electrical appliances, namely for passive design and thermal comfort, heating, refrigeration, and other electrical appliances. Therefore, a survey questionnaire method is appropriate in investigating the affordability of electricity for households in public PPR low-cost housing.
4.3.1 Sampling Techniques
Before determining the sample population, it is necessary to clearly define the elements available for the intended population, which can then be described as sampling units (Schofield, 1996). A sampling frame, which is used to organize the sampling unit, confines the population based on a specific element that identifies each sampling unit i.e. geographic locations, employment records, school class lists, voting register (Czaja & Blair, 2005; Schofield, 1996). Two basic strategies to ensure that a sample size reflects the targeted population, are probability sampling, and non-probability sampling (Buckingham & Saunders, 2004; Salant & Dillman, 1994; Schofield, 1996).
Probability sampling are also known as ‘random’ sampling (Buckingham & Saunders, 2004). Selecting a random sample size for the determined population can be determined randomly or systematically (Salant & Dillman, 1994). A simple random sampling (SRS) provides an equal opportunity for each member of the determined population to be selected (Barrow, 2006; Salant & Dillman, 1994). A SRS leads to a straightforward formulae to estimate the surveyed population’s parameters (Barrow, 2006). In comparison, a systematic sampling requires an additional step that involves a predetermined random start (Barrow, 2006; Salant & Dillman, 1994).
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Conversely, a non-probability sampling, also known as ‘purposive’ or ‘quota’ sampling, is dependent on a subjective judgement (Buckingham & Saunders, 2004; Salant & Dillman, 1994). A non-probability sampling carefully, but not systematically, selects the member for the sample based on predetermined characteristics (Salant & Dillman, 1994). A non-probability sampling can be used for exploratory research to discover new ideas, which will be systematically tested later (Salant & Dillman, 1994). For the purpose of this research, the random sampling method was adopted, as the fieldwork time frame was limited to four months for data collection.
4.3.2 Survey Types
There are four (4) general types of survey questionnaires - mail/postal questionnaires, internet/online surveys, telephone interviews, and face-to-face interviews (Czaja & Blair, 2005; Salant & Dillman, 1994; Wilson, 1996). However, in recent years, the use of computer aided devices to conduct questionnaires have gained popularity (Buckingham & Saunders, 2004). Computer aided devices enables the survey responses to be entered directly into a database, and provides an interactive feature that provides instant results (Buckingham & Saunders, 2004). Survey questionnaires can also be conducted by computer-assisted personal interviewing (CAPI), or telephone interviewing (CATI) (Buckingham & Saunders, 2004). With computer aided devices, the interview and data entry process is done simultaneously, which saves time, but also enables the researcher to rectify any inconsistencies in answers and coding error whilst conducting the interview (Buckingham & Saunders, 2004).
Closed ended questions with a pre-selected choice of answers do not require face-to-face interaction, and can be administrated through postal questionnaires, telephone interviews or the internet (Farrell, 2011). However, postal questionnaires as a method have been excluded due to time limitation and low respondent rate. Telephone interviews and internet surveys were also not considered suitable for this research based on the socio-economic characteristics of the targeted population, as telephones and internet access in these households are not considered widely available. In light of the above considerations, postal questionnaires, telephone interviews and internet surveys have been eliminated as possible survey methods, which leave the face-to-face interviews as the most suitable method of conducting the questionnaire. Additionally, the face-to-face method was deemed most suitable for this case study, as it encourages a
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4.3.3 Considerations and Limitations in Conducting a Survey Questionnaire
In determining which method is most suitable for a research survey, three (3) broad categories should be considered - administrative/resource, questionnaire issue, and data- quality (Barrow, 2006; Czaja & Blair, 2005). In terms of administrative and resource factors, the time and cost of conducting such surveys are crucial factors (Czaja & Blair, 2005). It is important that the researcher manages how much time is available to conduct a survey based on the sample size needed within a given research project (Czaja & Blair, 2005). Cost factors to consider include the hiring of persons to conduct the survey or to analyse the data collected; purchasing any hardware, software, and other supplies; and whether or not to provide incentives to the targeted sample population (Czaja & Blair, 2005).
In designing the questionnaire, essential factors include the amount of questions to be listed, and what kinds of questions are needed to answer the research questions and to achieve the research aims (Czaja & Blair, 2005). To determine the acceptable level of data-quality, issues such as response rate, accuracy, and biasness should be filtered and addressed when designing the questionnaire (Czaja & Blair, 2005). Survey questions must be simple, non-directive and unambiguous to avoid biased results (Buckingham & Saunders, 2004). Simple and easy to understand questions are also important as they best suit the specific socio-demographic of the lower-income household population. A short listed questionnaire encourages a higher response rate, as long-winded questions have the tendency to lower the response rate (Buckingham & Saunders, 2004; Czaja & Blair, 2005).
The reliability and validity of a survey is dependent on the research instrument used. In the case of a questionnaire, reliability is achieved when the same results are produced (uniformity), and validity is attained when the questionnaire successfully measures what it is designed to measure (Buckingham & Saunders, 2004). Reliability of data can be enhanced by using pre-coded questionnaires, as the data is analysed in exactly the same way (Buckingham & Saunders, 2004). A close or pre-coded question provides a set of pre-determined range of possible answers, while an open-ended question enables the respondent to answer freely and in their own words (Buckingham & Saunders, 2004).
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Open-ended questions increase the validity of a survey as it discloses insights on an issue from respondents (Buckingham & Saunders, 2004).
Conducting a face-to-face or computer-aided interview would raise the validity of data collected by simultaneous and continuous checking of data obtained, probing, and building trusts with respondents (Buckingham & Saunders, 2004). However, a more personalized interview risks decreasing the reliability of data, as interviewers may conduct an interview in a different manner and obtain different results (Buckingham & Saunders, 2004). Therefore, a short-listed and pre-coded questionnaire with pre- determined answers was considered as the most appropriate method, as it increases the reliability of the data collected.
4.3.4 Confidence Level and Sample Size
Finally, in determining the sampling technique and survey type, a sample size is calculated. Prior to calculating the sample size, the interval estimate, confidence level, confidence interval and percent of defect is determined (Bluman, 2012). An interval estimate is defined as “an interval or a range of values used to estimate the parameter”, while the confidence level is described as “the probability that the interval estimate will contain the parameter, assuming that a large number of samples are selected and that the estimation process on the same parameter is repeated” (Bluman, 2012 p.358).
The confidence level determines the confidence of the data collected within the sample size, and that the values are distributed in the population (Bluman, 2012). The three most common confidence level used are 90, 95 and 99% (Bluman, 2012). The confidence interval is the “specific interval estimate of a parameter determined by using data obtained from a sample and by using the specific confidence level of the estimate” (Bluman, 2012 p.358). The confidence interval range depicts the precision of the data collected or the margin of error, and it usually ranges between 0.05 (± 5%) or 0.03 (± 3%) (Mora, 2011; QI Macros, 2010). The sample size for the population of 27,102 units (described henceforth as “Building Stock”) can be determined for this case study context. Calculation for the sample size is determined by using the following statistical classification:
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Confidence interval of 0.05 (+/- 5%) Percent defects of 50% (split 50/50 response distribution) Using a random sampling approach with 90% confidence level, and a sampling error estimate (confidence interval) of +/- 5% (or 0.05) will enable a representation of the performance of the population being investigated (Barrow, 2006; Mora, 2011; Salant & Dillman, 1994). Therefore, the sample size for a known population (of 27,102 units), and with a defect rate of 50%, was calculated to be approximately 266 units (refer Table 4.1) (QI Macros, 2010). Details of the sample size calculation is presented in Appendix 4.1
Confidence Level (Power) 90% Confidence Interval 0.05 Population (if known) 27,102
Attribute Data
Percent defects (50%) 50% Sample Size (Unknown 269 Population) Sample Size for Known 266 Population
Table 4.1 Sample Size Calculation Source: (QI Macros, 2010)
The 266 individual units were divided between the two identified PPR low-cost housing projects, providing 133 units each. Selection of individual units was based on a systematic random sampling technique, in which the sampling fraction is based on the number of blocks (6 blocks) of each PPR housing project. The sample size of 133 units for each PPR low-cost housing project is divided by six (6) blocks, which provides an estimate of 23 units per block (refer Table 4.2) These 23 units will then be selected at random, depending on the availability and accessibility to respondents. Detailed protocol on the survey questionnaire will be explained in following subsections.
No. of Sample Year Total No. of Total. of No. of Units Name Attribute/ Constructed Units Block per block Variable PPR Taman Beringin 1999 1896 6 23 133 PPR Intan Baiduri 2000 1834 6 23 133 Total 3730 12 266
Table 4.2 Selected PPR Low-Cost Housing Characteristics and Sample Size
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4.4 Systems Boundary
Following the project-specific baseline key parameters, this section summarizes the research’s systems boundary, in reference to conducting the fieldwork. An analytical framework was created in order to collate the case study findings with the research aims. A systems boundary is defined to limit and narrow the scope of research to help guide the case study in terms of its execution and limitations. The systems boundary is guided by the justifications made in Chapters 1, 2 and 3, which are as follows:
1) Electricity consumption as primary indicator of GHG emissions, as it is the main energy source consumed in Malaysian buildings, 2) Low-cost housing in Malaysia is under-researched in terms of environmental consideration and has systematic GHG mitigation potential as it is governed by two specific standards, i.e. CIS 1 and CIS 2.
The systems boundary is subdivided into four categories, firstly determining its energy boundary, and then the building typology, the location and finally the life-cycle boundary (refer Figure 4.2). The consequent analytical framework will be further explained in the following section.
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Figure 4.2 Systems Boundary
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4.4.1 Energy Boundary – Electricity from National Grid
As described in preceding chapters, this research investigates electricity-related GHG emission, end-use pattern of consumption, and affordability of electricity. Electricity is the main type of energy consumed in the building sector (90%), and is increasing steadily in consumption (Energy Commission, 2011b). Therefore, the research project will only focus on electricity consumption from the national grid, provided by the national energy provider company (Tenaga Nasional Berhad – TNB) and the national energy balance provided by Energy Commission. Electricity bills collected from TNB was used to determine electricity consumption and its GHG related emission in the following case study, which is inherently bottom-up.
The national energy balance was used to determine electricity consumption and its related GHG emission for the building stock, which is appropriate as a top-down approach. These approaches are further elaborated within the analytical framework section.
4.4.2 Location Boundary – Kepong District in Kuala Lumpur
This research project focuses on Kuala Lumpur. With a population of approximately 1.6 million, it was considered 100% urbanized by year 2000 (Department of Statistics Malaysia, 2010; Jaafar, 2004). Kuala Lumpur’s rapid urban population growth since the early 1970’s has led to a legacy of housing problems, particularly for the lower-income population (Aziz, 2007). The City Hall of Kuala Lumpur (CHKL), the local authority of Kuala Lumpur, sets legal frameworks and regulatory processing systems for the territory (Rashid, 2002). With continuous rural to urban migration, Kuala Lumpur presents a relevant and pressing study of urban housing in the contemporary Malaysian context (Aziz, 2007).
Based on the list of PPR housing provided by City Hall Kuala Lumpur, there are 24 PPR low-cost housing projects within the Kuala Lumpur border, with a total of 27,102 individual apartment units (see Appendix 4.2) (City Hall of Kuala Lumpur, 2009a). From the population size, a typical characteristic of an average PPR low-cost housing project can be determined, such as average number of apartment units, number of building blocks, number of floors, etc. Therefore, an average PPR low-cost housing
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Characteristics of Typical PPR Low-cost Housing Project
Managed by: National Housing Department under Ministry of Housing and Local Government Housing project type: PPR Rent/PPR Own Building type: Low-cost apartment High-rise (17 storeys) Number of units: More than 1130 units Age of construction: >30 years (built after 1980) Location: Kuala Lumpur Climate: Tropical (hot-humid all year round : 22-33° C) Electricity supplier: Tenaga Nasional Berhad (TNB)
Table 4.3 Emblematic Case Study Similar Characteristics
4.4.3 Building Typology Boundary – PPR Low-Cost Housing Projects
As demonstrated in Chapter 3, the low-cost housing typology in Malaysia has received little attention for environmental performance research and development. Additionally, the absence of energy-efficiency legislation in the residential sector is potentially locking the building stock for long-term inefficient future. PPR low-cost housing has been selected as the focus of this research because it is produced and managed by governmental agencies (refer Table 4.4), and thus there is potential for uniformed and standardized policy improvements.
The study of this specific PPR housing typology is representative of Malaysian public low-cost housing, as its represents the largest percentage of public low-cost housing (39.2%) (City Hall of Kuala Lumpur, 2009a). Providing an energy performance baseline for this specific typology therefore makes a significant contribution to policy development. Additionally, as all low-cost housing developments must comply with the Construction Industry Standard 1 and 2 (CIS 1 and 2), providing energy requirements to this standard has the potential of being widely and uniformly disseminated.
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Residential Sector
Housing for Low-cost Low- Medium- High- cost the poor medium- cost cost
Private Sector Not relevant Private developers (N/R) (N/R) (N/R) (N/R) Cooperative societies
Public Sector (N/R) Sites & Services (N/R) (N/R) (N/R) Land Schemes Institutional Quarters Housing for hardcore poor – Program Perumahan Rakyat Termiskin (PPRT) People’s Housing Project - Program Perumahan Rakyat (PPR)
Table 4.4 Residential Sectors in Malaysia Source: Adapted from (Economic Planning Unit, 2006)
As defined in Chapter 3, the PPR public low-cost housing combined definition adopted in this research is ‘public low-cost housing that are sold for a maximum price of RM 45,000 per unit (PPR Own), or rented out for RM 124.00 per month for, households earning less than RM 4,000 per month (PPR Rent)’. Additionally, following the location boundary of Kuala Lumpur, the PPR low-cost housing projects are built as high-rise apartment flats, as designated for urban areas. Therefore, in determining the system boundary, the location of PPR low-cost housing projects is also important as the height (in storeys) differ according to urban and rural areas.
Choosing the specific location within the boundary of Kuala Lumpur was narrowed down by the location of existing PPR low-cost housing projects, and by data availability. In terms of data availability, the contact person in TNB was in charge of electricity billing for the western suburbs area of Kuala Lumpur (KL-West). Taking into consideration the average characteristics of a PPR low-cost housing project as consisting of 1,130 apartment units, 4 building blocks, and 17 storeys, the most suitable low-cost housing project within the KL-West area was determined as the case study. This was determined as two (2) PPR low-cost housing project that matched the average PPR characteristics in KL-West, which was in the district of Kepong (refer Table 4.5) i.e. PPR Beringin and PPR Intan Baiduri (also refer Appendix 4.2).
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Malaysia
West Malaysia (Peninsula) East Malaysia (Borneo) 1 Federal 11 States 2 Federal Territories 2 States Territory Putrajaya Kuala Lumpur Sabah & Sarawak Labuan
Not 11 Districts in 4 Zones relevant (N/R) (N/R) 10 Other Districts Kepong District (N/R) (N/R)
1 2 3 4
Table 4.5 Location Boundary for Kepong District Source: Adapted from (City Hall of Kuala Lumpur, 2009a).
4.4.4 Life-Cycle Boundary – Operational Phase
The operational phase of a building life-cycle represents the bulk of GHG emitted from the building sector, which is approximately between 80% to 90% (refer Figure 4.3) (UNEP-SBCI, 2010b) and is the primary focus of most building energy codes. Therefore, this research will only focus on GHG emission during the building operational phase of selected PPR low-cost housing units. Chapter 2 also justified the selection of UNEP- SBCI’s Common Carbon Metric (CCM) to measure operational and electricity-related GHG emission based on its universality, and its ability to generate baseline emission data from the least amount of information, but from a variety of sources.
The CCM was created to provide a universal tool to measure operational energy GHG emissions in a consistent and comparable manner (UNEP-SBCI, 2010b). Extensive international cooperation with leading world experts resulted in the development of a common system for measuring GHG emissions from building operations in two complementary approaches; one approach assesses building level performance (bottom- up) and the other assess GHG emissions at the regional/national level (top-down) (UNEP-SBCI, 2010b). The CCM measures end use energy-related carbon dioxide equivalent emissions from building operation, and provides a consistent and comparable data of energy intensity (kWh/m2/year) and carbon intensity (kgCO2e/m2/year or kgCO2e/o/year) (UNEP-SBCI, 2010).
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Life Cycle Stage I Life Cycle Stage II (80-90% emission) Life Cycle Stage III
Before-Use Phase Use Phase After-Use Phase Constru- Product ction
Transport Transport Transport Final disposal Final Deconstruction Transport Transport Manufacturing incorporated services incorporated Operation ofbuilding- Operation Raw material provision provision material Raw Construction Installation Construction Reuse, recycling, energy recovery recovery energy recycling, Reuse, Maintenance, repair, refurbishment repair, Maintenance, Operation of other appliances ofother Operation
Not included Included in the Common Carbon Metric & Protocol Not included
Figure 4.3 Operational Phase of Building Life-Cycle Included in the CCM Source: Adapted from (UNEP-SBCI, 2010b).
The before-use phase of a building’s life cycle, which includes processes like raw material production, transportation, and manufacturing (life cycle Stage I), and the after-use phase like deconstruction, recycling and disposal (life cycle Stage III) is not covered under the CCM (UNEP-SBCI, 2010b). The CCM is also based on a modular approach to allow for future expansion in the scope of emissions within the building sector, and recognizes that changes in emission boundaries may happen over different stages and time, and will require further definition (UNEP-SBCI, 2010b). Within the Stage II life cycle of building operation, the CCM measures three different scopes (refer Figure 4.3 and Table 4.6 ) or categories of emission, namely (UNEP-SBCI, 2010b p.24-26):
Scope 1: Direct, on-building-site or on-building-stocks GHG emissions Scope 2: Indirect on-building-site GHG emissions Scope 3: Other indirect GHG emissions
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Scope Source GHG
Direct Emissions 1 Power generation CO2, CH4 & N2O
Refrigeration and air-conditioning HFCs & PFCs equipment
Indirect Emissions 2 Consumption of purchased electricity CO2, CH4 & N2O
Purchased steam/heat CO2
3 Not included at this time CO2,
Table 4.6 Three Scope of Emission Covered in the CCM Source: Adapted from UNEP-SBCI (2010a)
Direct on-site emissions (Scope 1) are emissions from sources within the boundaries of the project studied (buildings or building stocks), which includes stationary combustion emissions, process emissions, and fugitive emissions (UNEP-SBCI, 2010b; WRI & WBCSD, 2004). Scope 1 emissions cover small building operations such as on-site electricity, refrigeration, boilers, heat or steam (UNEP-SBCI, 2010b; WRI & WBCSD, 2004). Fugitive emissions are emissions from intentional or unintentional releases of GHGs from the production, processing, transmission, storage and use of fuels (UNEP- SBCI, 2010b; WRI & WBCSD, 2004).
Indirect emissions (Scope 2) are emissions from activities which occur outside the boundaries of the building site, such as activities at a power plant (UNEP-SBCI, 2010b; WRI & WBCSD, 2004). Scope 2 emissions cover all GHG emissions connected to the generation of purchased energy, like electricity61 (UNEP-SBCI, 2010b; WRI & WBCSD, 2004). A study of Scope 2 emissions will help predict actual energy consumption in building operation, and can be used to identify areas for cost and emissions reduction (UNEP-SBCI, 2010b; WRI & WBCSD, 2004). Other indirect GHG emission (Scope 3) are emissions not covered in Scope 2, which are still relevant to building performance such as transport related activities within the building life cycle, re-use and recycling activities, and water disposal processes (UNEP-SBCI, 2010b; WRI & WBCSD, 2004). As asserted earlier, since electricity is the predominant type of energy used in the residential sector in Malaysia, investigating emissions from purchased electricity (Scope
61 The current CCM and Calculation Tool & Reporting Template does not include emissions from purchased heat/steam/cooling (UNEP-SBCI, 2010b)
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2) in the residential sector would be highly beneficial in efforts to reduce the national GHG emissions.
In summary, based on the systems boundary set in place, the case study is limited to: i) Indirect GHG emissions and end-use consumption pattern of purchased electricity, ii) Electricity purchased from Tenaga Nasional Berhad, and iii) Two public PPR low-cost housing projects in the Kepong district of Kuala Lumpur.
4.4.5 Considerations and Limitations Prior to Conducting Case Study
An important consideration prior to conducting fieldwork is the time needed for planning, data collection and data analysis. The fieldwork duration to conduct the case study was set at approximately three (3) months. Therefore, execution of the survey questionnaire and collecting electricity bills needed to be planned and scheduled within the allocated time frame. The design of the survey questionnaire was completed prior to the fieldwork phase, to ensure time efficiency.
Apart from time restrictions, other consideration and limitations identified in Chapter 1 were administration and resources required to conduct the survey questionnaire. Approval from the City Hall of Kuala Lumpur was needed, in order to conduct the survey questionnaire within the identified PPR low-cost housing under their jurisdiction. Therefore, the duration of time required by City Hall of Kuala Lumpur for processing applications and issuing approvals must be incorporated within the work schedule of executing the fieldwork. Other contingencies such as meeting with City Hall of Kuala Lumpur officials for approval, and delays in approval also need to be considered when planning the fieldwork schedule.
In addition, the identification and cultivation of a contact from TNB willing to supply electricity bills for the calculated minimum sample size was essential. In this research context, the contact person in TNB was identified within the pilot stage of the research (refer Piloting the Case Study section). Scheduling an allocated time with the contact person to access electricity bills must also be considered when planning for the fieldwork schedule. Finally, the work schedule must incorporate ample time for executing both the survey questionnaire, and collecting the electricity bills. The fieldwork execute plan is attached in Appendix 4.3
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In order to investigate operational affordability, a thorough investigation into the actual payment for electricity expenditure with the household’s total income is needed. This would require extensive data collection and connecting each electricity bill to the specified household unit. It would also require a lengthy collaboration between the energy provider companies with the specified household unit’s occupant, which is dependent on whether the household would share such information. Therefore, in the context of this research, household expenditure for electricity and other amenities are assumed as the billed amount, and not actual payment.
4.5 Summary
In summary, this Chapter has presented the systems boundary in terms of the specific focus of this research and its limitation, and the research design in terms of the two methods of investigation i.e. the UNEP-SBCI’s Common Carbon Metric and a survey questionnaire. This Chapter has also identified the various data needed to establish energy & GHG baselines and an indicator of the affordability of electricity consumption. The UNEP-SBCI’s Common Carbon Metric is used to calculate GHG emissions through end-use electricity consumption data collected from electricity bills over a one year period. This data is used to provide a project-specific baseline for the case study. A survey questionnaire is most appropriate to investigate end-use electricity consumption pattern per household, the operational costs of electricity, water and rent, and conducted via face-to-face survey with a short listed and closed-ended questionnaire.
This methodology provides a quantified baseline of GHG emissions associated with electricity consumption, and an analysis of end-uses and operational affordability. The specific systems boundaries identified are electricity from national grid (energy), in the District of Kepong in Kuala Lumpur (location), for high-rise public PPR low-cost housing projects (building typology) and calculating operational GHG indirect emission (building life cycle). Pilot studies were conducted to determine the availability and accessibility of data, and to test the CCM workability in the systems boundary.
These data are necessary to support policy development and in providing actual building performance and household operational cost for rent, electricity and water. The data collected can also help to identify design features that reduce energy and electricity
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Chapter 5: Case Study Protocol
5.1 Introduction
This Chapter presents the protocol used to collect data for the case study. As presented in Chapter 4, two methods of investigation were used to realize the research objectives. This chapter is accordingly divided into two main sections, the UNEP-SCBI’s Common Carbon Metric and the survey, using a questionnaire. Extensive fieldwork was carried out between November 2011 to March 2012, to concurrently collect electricity bills and conduct the survey (refer Table 5.1). The process of identification, obtaining permission, and scheduling the fieldwork took approximately three weeks.
Chapters 4 explained the need for a project-specific baseline and a case study methodology. The analytical framework in Chapter 4 described what data would be needed and how it would be analysed to reach the aims of this research. Based on the project boundary, the case study focussed on PPR low-cost housing projects in Kepong district of Kuala Lumpur. Both PPR low-cost housing projects are located North West of the city centre of Kuala Lumpur. PPR Intan Baiduri and PPR Beringin are located approximately 15.7 km and 13.5 km from the city centre, respectively (refer Figure 5.1).
Table 5.1 Fieldwork Execution Plan
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A. PPR Intan Baiduri Approximately 15.7 km away from City Centre
B. PPR Beringin: Approximately 13.5 km away from City Centre
C. City Centre, Kuala Lumpur, Federal Territory of Kuala Lumpur, Malaysia
Figure 5.1 Location of Case Study in Kuala Lumpur Boundary Source: (Google Map, 2012)
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5.2 Adopting the UNEP-SBCI Common Carbon Metric
The UNEP-SBCI’s Common Carbon Metric (CCM) has been adopted as the method of investigating GHG emissions from operational electricity consumption and this section explains the CCM methodology. As discussed in Chapter 3, the Malaysian Green Building Index (GBI) building assessment tools have not included calculations for operational GHG emissions through end-use energy consumption. While other building assessment tools exist, none have established an internationally comparable and consistent in full MRV framework for GHG emissions reporting (Beattie et al., 2008; Ng et al., 2013; Reed et al., 2009).
The CCM requires the collection of electricity bills for the identified sample size to measure their energy performance data and operational GHG emissions. The electricity bills were collected from Tenaga Nasional Berhad, (TNB) which is the main electricity provider company that supplies to the grid in Peninsular Malaysia. There are three data sets for both the top-down and bottom-up approaches, i.e. characteristics, electricity consumed, and amount of fuel consumed. The data collected for both top-down and bottom-up approaches are briefly explained in Figure 5.2. The CCM requires electricity bills to be collected for one year. The utilized electricity in terms of kWh is converted into kWh/m2 or kgCO2e/m2 or kWh/occupant or kgCO2e./occupant, depending on data availability (UNEP-SBCI, 2010b). Findings from applying the CCM are presented in Chapter 6.
5.2.1 Direct and Indirect Consumption of Purchased Electricity
The CCM enables analysis of both direct and indirect consumption of purchased electricity for the building sector in order to estimates indirect emissions (UNEP-SBCI, 2010b). Indirect emissions are a “consequence of the activities that occur outside the building site, for example activities at a community power plant for providing the energy consumed on-building-site” (UNEP-SBCI, 2010b p.26). Indirect emissions also include “all GHG emissions associated with the overall generation of purchased energy such as electricity” (UNEP-SBCI, 2010b p.26). Measuring and reporting indirect emissions can be used to “gauge energy usage and identify areas to reduce costs and emission” (UNEP- SBCI, 2010b p.26).
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Figure 5.2 UNEP-SBCI Common Carbon Metric Data Collection Protocol Adapted from UNEP-SBCI (2010)
The CCM tool assesses building performance by collecting data from both the top-down and bottom-up approaches. The CCM generates a performance baseline using both approaches. The IPCC’s Fourth Assessment Report (AR4) includes both top-down and bottom-up approaches to assess GHG potential and mitigation costs through different sectors (Hoogwijk et al., 2010; IPCC, 2007b; Ürge-Vorsatz & Novikova, 2008), which is consistent with the CCM’s methodology. Additionally, performance baselines help present MRV emissions reduction (UNEP-SBCI, 2010b). The CCM defines a performance baseline as “the actual measured performance of a building or aggregation
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Electricity bills were then collected from TNB for both PPR Intan Baiduri and PPR Beringin using a random sampling approach, which provides equal opportunity for each member of the determined population to be selected (Barrow, 2006; Salant & Dillman, 1994). A visual representation of implementing the CCM in the case study context of this research is presented in Figure 5.3.
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Figure 5.3 Implementing UNEP-SBCI's CCM into Case Study Adopted from UNEP-SBCI, 2010
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5.2.2 Data Collection for CCM Top-Down Approach
Top-down models set GHG targets and introduce fiscal policy tools to reduce energy consumption and GHG mitigation potential by statistically analysing the different sectoral baselines (van Vuuren et al., 2009). Top-down approaches also measure the national gross energy flow, while the bottom-up approach measures electricity bills from the selected low-cost housing projects (UNEP-SBCI, 2010). Top-down analyses often rely on real-world market data to approximate aggregated relationships between inputs of the economy (relative costs, relative market shares) and sectoral outputs (Jaccard et al., 2004).
A top-down modelling approach aims to fit a historical time series of national energy consumption or CO2 emissions data and therefore often lacks current and future details or technological options (Kavgic et al., 2010). It emphasizes macroeconomic trends and relationships experienced in the past rather than individual or physical factors, which influence energy consumption in buildings (Kavgic et al., 2010). The UNEP-SBCI CCM top-down approach requires city or national level data on gross energy use and information about building stock which includes building type, gross floor area and occupancy (if available) (UNEP-SBCI, 2010b). The top-down approach calculates the performance of the *Whole by using distinctive data on patterns of energy consumption, total area size, and occupancy (UNEP-SBCI, 2010b).
*Whole is defined as a group of buildings, and may represent a city, in which case of boundary of the *Whole is the physical boundary of the city’s limits. This research adopts the latter definition and the Kuala Lumpur public low-cost housing portfolio is considered the *Whole. However this research will adopt the term “*Building Stock” due to the focus on a particular typology of building, that is public PPR low-cost housing in Kuala Lumpur. Collecting data for the top-down approach follows a series of steps:
1) Total area of the *Building Stock (m2) (in terms of building floor area); 2) Total occupancy of the *Building Stock; 3) Percentage (%) of occupants to building area; 4) Total electricity consumed by the *Building Stock; 5) Total fuel consumed by the *Building Stock; and
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6) Percentage (%) of electricity and fuel attributed to different categories of building stock (UNEP-SBCI, 2010b).
STEP 1
The 24 PPR public low-cost housing projects in Kuala Lumpur provide a total population size of 27,102. This is the *Building Stock, as described above. PPR Beringin and PPR Intan Baiduri then provided a minimum sample size of 266 units of households. Electricity bills were then collected from TNB for both PPR Intan Baiduri and PPR Beringin using a random sampling approach, which gives an equal opportunity to each member of the total population to be selected (Salant & Dillman, 1994).
Firstly, the total area of the *Building Stock is calculated by multiplying the average unit size (63m2) with the total number of units of public PPR low-cost housing in Kuala Lumpur (27,120 units). Equation 5.1 summarizes the calculation. As specified in Construction Industry Standard 2 (CIS 2), the average unit size of 63m2 is measured as net floor area, which excludes floor area of stairwells, stairs, lifts, lift lobbies, and foyers (CIDB, 1998). The net floor area of 63m2 is measured from centre line of the walls (CIDB, 1998). The total occupancy of the *Building Stock can be calculated by multiplying the mode62 of each household unit’s occupants, derived from the survey findings.
Estimate total floor area (m2) = Net floor area per unit (m2) x Number of household units
Equation 5.1 Calculated Total Area (m2)
STEPS 2 and 3
Secondly, the total occupancy of the *Building Stock was calculated. However, information regarding household occupancy was unavailable. Therefore the household total occupants for the *Building Stock was calculated by defining the mode of sample size for household occupants, and multiplying it with the total number of units (refer Equation 5.2). The mode of a household is derived from the survey findings, and the combined data inputted in STEPs 1 to 3 (refer Appendix 5.1a).
62 Mode is defined here as “the most frequent value of a set of data” (Merriam-Webster, 2011).
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Estimate total number of occupants = Mode of Occupant per household (persons) x Number of household units
Equation 5.2 Calculated Total Number of Occupants
STEP 4
Next, the percentage (%) of electricity and fuel attributed to the building stock is calculated by disaggregated national data on the building stock’s typology. The top-down approach involves an estimation of electricity consumption for the entire population of PPR public housing in Kuala Lumpur. It is recognized that these data might not be easily available from local energy, electricity or gas providers. In cases where relevant information is not accessible, it may be obtained from permit records or energy reporting programmes (UNEP-SBCI, 2010b).
To estimate the total electricity consumed by the *Building Stock, aggregated data of electricity consumption by the residential sector is needed, and is collected from the National Energy Balance online database set up by the Energy Commission via the Malaysian Energy Information Hub (MEIH). The national electricity consumption data is aggregated by sector, i.e. agriculture, commercial, transport, industrial, and residential (Energy Commission, 2011b). Many other studies (Kavgic et al., 2010; Nässén et al., 2007; Tuladhar et al., 2009; van Vuuren et al., 2009) have calculated energy use using the top-down approach and disaggregated national energy statistics.
Consequently, in order to measure a more accurate percentage of electricity consumed by the *Building Stock, national residential property stock data in needed, which will then indicate the percentage of the *Building Stock as compared to the national total stock (also refer Kavgic et al., 2010; Nässén et al., 2007). This was collected from the Annual Property Stock Report from the National Property Information Centre (NAPIC) online database by the Valuation and Property Services Department (NAPIC, 2012). The data is then inputted as STEP 4 of the top-down approach, and converted to total emissions (metric tonnes GHG) (refer Appendix 5.1b).
STEPS 5 and 6
STEP 5 and 6 of the CCM top-down approach is to input data on fuel type consumed by the *Building Stock (UNEP-SBCI, 2010b). The fuel consumption data is broken down to
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The performance of the *Building Stock is then tabulated in a summary table that presents the combined emissions (in metric tonnes of CO2e) and energy consumption (in kWh) (UNEP-SBCI, 2010b). The *Building Stock’s energy intensity and carbon emissions are then further summarized by area and by occupant which can also be presented as a graph (kWh/m2 or kgCO2e/m2 or kWh/occupant or kgCO2e/occupant) as illustrated in Appendix 5.1g (UNEP-SBCI, 2010b). Therefore, using a top-down approach offers a broader scale projection of Malaysia’s building stock future based on different scenarios, with or without any mitigation strategies implemented. Subsequently monitoring the effectiveness of current GHG mitigation measures for the building stock is essential to Malaysia’s pledge to reduce 40% of its GHG emissions by 2020.
5.2.3 Data Collection for CCM Bottom-Up Approach
A bottom-up approach requires more technical and sectoral data which is usually derived from physical indicators, while a top-down approach describes the process of energy and electricity flow within an economy (Hoogwijk et al., 2010). A bottom-up approach utilizes data from physical evidence to measure the end-use GHG emissions during a building’s operational phase (Hoogwijk et al., 2010). The emissions data can subsequently be used to identify the potential for mitigation via physical design. Bottom-up methods consist of disaggregated components that are usually presented in a hierarchy, which are then combined to estimate how various individual elements impact the results. For example it allows the researcher to determine which type of heating system is more energy efficient, and how that subsequently reduces GHG emission (Kavgic et al., 2010).
This approach however, requires extensive empirical data to support each component’s description (Kavgic et al., 2010). In energy and environmental policy modelling, bottom-
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The CCM Calculation Tool and Reporting Template includes country specific emission factor to reflect the GHG emissions from the local energy mix used to generate the electricity (UNEP-SBCI, 2010a). Malaysia’s emission factor is tabulated at 0.6190755 kgCO2/kWh, for electricity purchased from a grid for 2007 onwards (UNEP-SBCI, 2010b). The GHG emissions can be calculated through Equation 5.3, as follows*:
Amount of Country-specific electricity CO e CO emissions 2 purchased 2 factor emissions (e.g., kWh)
Equation 5.3 GHG Emissions Based on Metered Electricity Use Source: (UNEP-SBCI, 2010a)
While the basic building stock information is aggregated in the building checklist, collecting the electricity bills utilized for the specific building can be dealt with separately which provides flexible phases of fieldwork investigation. The CCM bottom- up summary compares individual building performance against all buildings that are samples from the same building category (e.g. a hotel would be compared with all hotels within the sample). In terms of energy intensity (kWh/m2 and kWh/occupant) and carbon emissions (kgCO2e/m2 and kgCO2e/occupant) (UNEP-SBCI, 2010b).
* Note: CH4 and N20 emissions are not quantified in this equation due to their varying quantities, equipment efficiency, vintage of combustion technology, and maintenance and operational practices. There are also no current emissions factors available for CH4 and N20, while CO2 emission factors are updated on annual basis by the International Energy Agency (IEA) (UNEP-SBCI, 2010 p.15).
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The CCM worksheet calculates the performance of individual building(s) and the samples designed to be statistically representative of the *Building Stock (UNEP-SBCI, 2010b). With the calculated sample size of 266 units, the data will be a representation of the population of 27,102 PPR low-cost housing units in Kuala Lumpur. The CCM also compares the performance of individual buildings against:-
i) the mean performance of buildings sampled from the same category so far; ii) the performance of the *Building Stock based on the high-level data entered in the top-down approach; (UNEP-SBCI, 2010b); and iii) for presentation/visual purposes.
The CCM bottom-up approach requires specific data inputs according to the various STEPs 1 to 5 and listed as (also refer Appendix 5.2a):
1) Building characteristics; 2) Occupancy; 3) Area (m2) (in terms of floor area); 4) Total amount of purchased metered electricity (kWh); and 5) Total amount of different fuels consumed (segmented by type) (UNEP-SBCI, 2010b).
STEPS 1-3
Building characteristics, such as year of construction and address were gathered from City Hall, Kuala Lumpur (City Hall of Kuala Lumpur, 2009a). The total area of the sample size is calculated by multiplying the average unit size (63m2) with number of units for the sample size (also refer Equation 5.1). These data are then inputted in the CCM bottom-up approach STEPs 1-3 (refer Appendix 5.2b). The total number of occupants was also calculated, similar to the top-down approach. This estimate is derived from data collected from the survey instrument to determine the mode of household occupancy and multiplying it with the number of units within the sample size (also refer Equation 5.2).
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STEPS 4 and 5
Specific data for electricity use was acquired from Tenaga Nasional Berhad, by collecting electricity bills of individual units for the sample (Tenaga Nasional Berhad, 2012). By using a random sampling technique, one unit from each floor within the 17 storey block was selected, which gives equal opportunity for each member of the determined population to be selected (Barrow, 2006; Salant & Dillman, 1994) (also refer Chapter 4, sub-section 4.3.1). The sampling fraction will be based on the number of 6 blocks of each PPR housing project (refer Appendix 5.2c). This data is then tabulated to calculate the total electricity consumed for 12 months, for the 12 blocks of each PPR housing project (STEP 4). Subsequently, the electricity consumption sum of each block is calculated, and finally the total for each PPR low-cost housing project for the 12 months duration.
The total electricity consumption for each PPR low-cost housing project is tabulated (refer Appendix 5.2d) together with calculated total floor area obtained from Equation 5.1. This estimate is then inputted into the CCM bottom-up approach STEP 5, together with Malaysia’s electricity emission factor (0.6190755 kg CO2/kWh) (refer Appendix 5.2e). Energy consumption and GHG emissions are calculated and presented in terms of total GHG emissions (tonnes CO2e), detailed in Appendix 5.2f (UNEP-SBCI, 2010b).
The performance metric presented by both the CCM’s top-down and bottom-up approaches provides a calculated baseline of electricity consumption and GHG emission performance for the PPR low-cost housing typology in Kuala Lumpur. Adopting the CCM will enable similar baselines to be generated for other typologies within the residential sector, and the building sector at large. Consequently, the performance baseline will inform policy development, based on presented measurable, reportable and verifiable data for operational energy consumption and its related GHG emissions.
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5.3 Survey Protocol for Patterns of End-Use Consumption and Affordability of Electricity
A survey questionnaire63 was conducted to determine what percentage of household income is spent on rent/housing loan repayment, electricity and water64. Measuring household electricity bills and monthly household income provides the proportion spent on electricity, as an indicator of operational and long term affordability. The questionnaire was also used to collect data to estimate the average hourly usage of conventional household electrical appliances based on their primary function
In order to acquire a high response rate, a face-to-face method is more appropriate in dealing with different levels of literacy, as it enables to researcher to further clarify the questions (Preston, 2009). Similarly, using a combination of short and closed-ended questions, together with face-to-face interviews helped ensure a high response rate. Additionally, to reduce and avoid biased answers, the questionnaire included multiple choices based on a range of average daily use of electrical appliances between 0 to 24 hours, monthly household income scaled from under RM 1,500 to RM 4,000 or more per month and monthly household expenditure for electricity and other amenities between RM 150 to RM 400 or more per month.
Anonymity needs to be managed in order to avoid biased answers to the questionnaire (Schofield, 1996). Therefore, the survey only records the unit number of households and not the registered name of the household. It was also made clear to respondents that the results of the survey would only be used for this research project, and was independent of any governmental agency. These measures were deliberate steps taken to increase trust between the researcher and the respondents participating in the survey.
A proposed method of using a computer-aided-device (iPad version 2) was considered to help maximize time efficiency in collecting and analyzing the data. ‘Polldaddy Pro’ software that was created specifically for the iPad tablet computer (Polldaddy, 2012). The data collected from the software can also be exported to Excel Word format to be further analysed.
63 The survey questionnaire is included in Appendix 5.2 64Only electricity and water utility expenditure will be included in the survey questionnaire as Malaysia is located in the tropical climate region where heating isn’t a priority.
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The Polldaddy Pro software was also used to compile data for the 281 households surveyed, in terms of calculating the percentage of tenants, and number of occupants living in households (Polldaddy, 2012). This enables the data to be further manipulated in calculating the total, average number or mode of persons living in an average household.
However, the ‘Polldaddy Pro’ software was unable to retract answers once the question has been answered. The inability to correct an answer for the survey during the fieldwork was considered unsuitable. Therefore, the survey was conducted on paper as a questionnaire, in order to ensure accuracy of the respondents’ answers. A short and straight forward questionnaire was designed, divided into three sections (refer Figure 5.4):
1) Basic demographics; 2) Average daily consumption of end-use electricity for certain appliances; and 3) Income and operational costs.
The three categories were informed by precedent studies in investigating the electricity affordability or households operating expenditure by Chitnis, et al. (2012) and Ferguson & MacLean (2011), and end-use energy consumption patterns by Streimikiene & Volochovic (2011), Girod & de Haan (2009), and Tang (2005).
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Figure 5.4 Survey Protocol
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5.3.1 Basic Demographics
The basic demographics section of the questionnaire gathers information pertaining to the status of occupancy (owner, renter, or in-transit), number of persons, and age range of occupants living in the household unit (refer Appendix 5.4a). As mentioned earlier, the name of the respondent was not recorded to retain anonymity. Some PPR housing units are allocated for households that are relocated from government land acquisition (National Housing Department, 2008)., which consequently leads to transitory tenancy situation.
The occupancy mode of an average household is calculated from this section of the questionnaire. Establishing the mode of an average household is done by estimating the total occupancy of the sample size, and the *Building Stock, which is needed for the CCM calculations. Findings from the survey are presented in Chapter 7.
5.3.2 Investigating Average Daily Consumption of Electrical Appliances
The second section of the questionnaire collects data required to accurately estimate the average operating time (hourly usage) of generic household electrical appliances based on their primary function. It permits generalized policy recommendations to be made. The daily average operating time is rated on a hourly scale, starting with 0-1 hour, 1-2 hours, 2-3 hours, 3-4 hours, 4-5 hours, 5-6 hours, above 6 hours, and finally 24 hours (refer Appendix 5.4b). The final section of the questionnaire determines monthly household income, and the average percentage spent on the monthly electricity bill.
End-use electrical appliance are based on seven different types of electrical appliances, i.e. lighting, cooling and ventilation, heating, refrigeration, entertainment equipment, cooking and kitchenware, and washing clothing (washing machine and/or dryer). These seven categories of appliances can be combined into five primary functions, i.e. artificial cooling, artificial lighting, heating, refrigeration, and other miscellaneous electrical functions (refer Figure 5.5) Analysis of the data collected will identify which electrical appliance is utilized most in the average household, according to the average operating time.
Findings from this section are tabulated to showcase average hourly consumption per day as a percentage of total households electricity use (refer Appendix 5.4c). This
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Figure 5.5 A Framework for Recording End-Use Patterns of Electricity Consumption
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5.3.3 Investigating Long-Term Operational Affordability
The third part helps assess the affordability of electricity and other amenities for low- income households. Monthly household income and household expenditure for electricity, water and other amenities such as telephone and internet is surveyed. This accordingly indicates long-term and operational affordability of these public PPR low- cost housing projects, which are funded and managed by governmental agencies.
The operational affordability definition adopted for this research was determined in Chapter 3 as the percentage of monthly household expenditure to monthly income as shown in Table 5.2 below. Analysis of the data was compared against this affordability definition. Findings from this section accurately indicate housing affordability for these PPR households, in terms of operational costs and long-term affordability.
Apportionment of Operational Cost to Average Monthly Household Income (%) International Benchmark Survey Findings Rent 30 % Electricity 10% Other Utilities 6%
Table 5.2 Analysis of Affordability
The household monthly income range will be between ‘under RM 1,500 to RM 4,000 or more’ per month. The income scale for low-cost housing in Kuala Lumpur was provided by the City Hall of Kuala Lumpur (City Hall of Kuala Lumpur, 2000). The scale for the average monthly electricity bill and other housing expenditures are adjusted to match 10% of the income scale, from RM 150 to RM 400 per month (refer Appendix 5.4d).
5.4 Summary
Chapter 5 has presented a protocol for executing the case study’s field work investigation through the CCM and a survey instrument. The CCM tool collates data both from a top-down approach and bottom-up approach, in order to measure energy performance and GHG emissions for the defined sample size or the *Building Stock. The questionnaire is used to obtain data on end-use electricity consumption for electrical appliances based on primary function (i.e. lighting, cooling and ventilation, heating,
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The CCM’s bottom-up approach was used for measuring actual GHG emission from householders’ day-to-day use of electricity (UNEP-SBCI, 2010b). A performance baseline for the *Building Stock was then developed through top-down aggregation of national energy balance data (UNEP-SBCI, 2010b). This was used to produce a baseline of electricity consumption and associated GHG emissions for that building typology. The results also present an opportunity for the results of this research to be generalized to all PPR housing as the design of PPR low-cost housing projects are standardized nationally. The next chapter presents the results of the case studies.
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Chapter 6: Baseline Electricity Consumption & Associated GHG Emissions: Results from the Common Carbon Metric
6.1 Introduction
Chapter 6 presents findings of the case study of two public PPR low-cost housing projects in the district of Kepong, Kuala Lumpur. The case study findings are separated by method of investigation, i.e. Chapter 6 presents the findings using UNEP-SBCI’s Common Carbon Metric (CCM) and Chapter 7 presents the findings of the survey using the questionnaire presented in Chapter 5 – Appendix 5.4. Chapter 6 discusses the case study findings of PPR Beringin and PPR Intan Baiduiri, in terms of their end-use electricity consumption and related GHG emissions. The case study was conducted over 17 weeks (November 2011 to March 2012) during which time both the survey was performed and the electricity bills collected. The calculation of total amount of electricity and energy consumed is described separately between the top-down and bottom-up approach.
6.2 Implementing the UNEP-SBCI CCM in Case Study
The process of collecting electricity bills from Tenaga Nasional Berhad (TNB) took approximately five weeks, between 4th January 2012 to 15th February 2012. The electricity bill data was obtained with the authorization of the office of Meter Reader Examiner for KL-West Area, under the Distribution Division of TNB. A permission letter was also obtained from Housing Management Department in City Hall of Kuala Lumpur, to conduct the survey within the PPR low-cost housing projects under their management.
The 24 PPR public low-cost housing projects in Kuala Lumpur provided a total population size of 27,102 units, for the purpose of this research and catering for the CCM, this amount is identified as the *Building Stock. From this population, it provided a minimum sample size of 266 households units. The selection of PPR Beringin and PPR Intan Baiduri in the district of Kepong were discussed in Chapter 4.
The collection exceeded the minimum sample size of 266 household units, where a total of 383 household unit’s electricity bills were collected between PPR Beringin and PPR Intan Baiduri, with 181 units for PPR Beringin and 202 units for PPR Intan Baiduri. Surpassing the minimum sample size and additional to the statistical classification
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6.2.1 Top-Down Approach
The total floor area of Building Stock (STEP 1) is calculated at 1,707,426 m2 (refer Equation 6.1). Using the mode of occupants per household (5 persons) and multiplying it with total units within the Building Stock (27,102 units) provides a calculated total number of occupants (STEP 2) of 135,510 persons (refer Equation 6.2). These data are then inputted into the CCM top-down approach STEPS 1-3 (refer Appendix 6.1a).
STEP 1
Equation 6.1 Calculated Total Floor Area for the Top-Down Approach
STEPS 2-3
Equation 6.2 Calculated Total Number of Occupants for the Top-Down Approach
65 Mode is defined here as “the most frequent value of a set of data” (Merriam-Webster, 2011).Only in Methods
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STEP 4
Electricity consumption of the residential sector *Building Stock is calculated from the disaggregated data collected from NEB and the Annual Property Stock Report that states the total existing residential stock. The total electricity consumption for 2010 was at 104,588.59 GWh, and the residential sector consumed approximately 21% of the total at 22,527.31 GWh (refer Table 6.1) (Energy Commission, 2011b). The electricity consumed by the residential sector is then disaggregated further according to the percentage of the *Building Stock within the total residential stock.
National Electricity Consumption for 2010 Gigawatt Hour (GWh) Percentage (%)
Agriculture (GWh) 279.12 0.27%
Commercial development (GWh) 3,5122.6 33.58%
Transport (GWh) 209.34 0.20%
Industrial development (GWh) 46,450.22 44.41%
Residential development (GWh) 22,527.31 21.54%
Total 104,588.59 100%
Table 6.1 National Electricity Consumption for 2010 Source: Energy Commission (2011b).
The total existing national residential stock for 2010 was at 4,433,310 units66 (NAPIC, 2012) (refer Appendix 6.1b). Therefore, the total of 27,102 units for *Building Stock represents approximately 0.61% of the total for 2010 (refer Table 6.2). Consequently the electricity consumption for the *Building Stock is then calculated at 0.61% of the total electricity consumed by the residential sector (22,527.31 GWh), which amounts to approximately 137.42 GWh (also refer Table 6.2).
Electricity Consumption for Total Electricity Total Units *Building Stock Consumption
Total Residential Sector 4,433,310 units 22,527.31 GWh
27,102 units Represents 0.61 % of Total *Building Stock 137.42 GWh Residential Stock
Table 6.2 Calculated Electricity Consumption for the *Building Stock Source: Energy Commission (2011b).
66 Data specific for 2011 is unavailable via both sources (Energy Commission, 2011b; NAPIC, 2012). The latest data available for both sources are only up to year 2010, therefore the data presented should be acknowledged as an assumption.
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This data is then inputted into the CCM top-down approach STEP 4, together with Malaysia’s emission factor of 0.6190755 kgGHG/kWh (UNEP-SBCI, 2011) (refer Appendix 6.1c). This consequently provided a calculated total GHG emissions of
85,073.36 metric tonnes (MtCO2e.) of GHG (UNEP-SBCI, 2011) (refer Appendix 6.1d).
STEP 5
STEP 5 of the CCM top-down approach acquires data on fuel type consumed by the *Building Stock (UNEP-SBCI, 2010b). Data for the fuel type in the generation mix for electricity produced by Tenaga Nasional Berhad (TNB) for the national grid in Peninsula Malaysia is collected from the Annual Performance and Statistical Information Report by the Energy Commission (Energy Commission, 2010). The fuel consumption data is a generalized fuel type consumed in the national grid (refer Table 6.3). The applicable fuel type disaggregation (i.e. natural gas, oil and diesel) is then inputted as STEP 5 of the CCM top-down approach (refer Appendix 6.1e and 6.1f). It must be noted that the ‘hydro fuel’ type cannot be handled in the current CCM tool so the calculations are only based on natural gas, oil and diesel.
Fuel Type Consumed for Percentage of Fuel Fuel Consumption for *Building Stock Total 2010 (GWh) (%) Consumption of 137.42 GWh
Hydro 5,227 18.88 % 25.94 GWh Natural Gas 22,337 80.67 % 110.86 GWh Coal - 0 % 0 Oil 6 0.02 % 0.03 GWh Diesel 119 0.43 % 0.59 GWh Total 27,689 100 % 137.42 GWh
Table 6.3 Fuel Type in Generation Mix for Electricity Produced by TNB and Calculated Fuel Consumption for *Building Stock Source: Energy Commission (2011b)
STEP 6
Finally, the top-down performance metrics summary is presented in terms of energy consumed (kWh/m2/yr or kWh/occupant/yr) and GHG emissions (kgCO2e./m2/yr or kgCO2e./occupant/yr) for the *Building Stock. The performance metrics provided the energy consumption estimate of 133 kWh/m2/yr or 1674 kWh/occupant/yr, and GHG emission of 61 kgCO2e./m2/yr or 761 kgCO2e./occupant/yr (UNEP-SBCI, 2011) (refer
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Table 6.4) (also presented in more detail in Appendix 6.1g). A visual representation of the summary is presented in a graph (refer Appendix 6.1h).
Performance metrics: By area By occupant
kg CO e. / occupant / kWh / m2/ yr kg CO e./ m2 / yr kWh / occupant / yr 2 2 yr
Total residential Single-family residential
Multi-family residential 132.88 60.41 1674.31 761.15 Other residential Total non-residential Total (Based on data on the *Building Stock) 80.48 49.83 1014.09 627.80
Table 6.4 Results Summary of Top-Down Approach for *Building Stock Source: (UNEP-SBCI, 2011)
6.2.2 Bottom-Up Approach
The estimate total area surveyed was calculated by multiplying the individual net floor space of 63m2 to the number of units sampled (383 units). The calculated total floor area of PPR Beringin and PPR Intan Baiduri is calculated at 11,403 m2 and 12,726 m2 respectively (also refer Equation 6.1 and Table 6.5).
Average Mode of Total Monthly Net Floor Area Total Monthly Constructed Units Occupant Electricity Address Area per Surveyed Occupants Electricity (Year) Surveyed per Consumption Unit (m2) (m2) Surveyed Consumption Household (kWh) (kWh) PPR 1999 63 181 11,403 5 905 480,616 221.96 Beringin PPR Intan 2000 63 202 12,726 5 1,010 513,065 211.90 Baiduri
Total 383 24,129 5 1,915 993,681 216.93
Table 6.5 Data Collection for the Bottom-Up Approach STEP 1-3
STEPS 1-3
Using the mode of occupants per household (5 persons) and multiplying it with total units surveyed for 181 units for PPR Beringin and 202 units for PPR Intan Baiduri provided an calculated total number of occupants of 1,915 person (also
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Equation 6.2 and Table 6.5). These data are then inputted in the CCM bottom-up approach (STEPS 1-3) (refer Appendix 6.2a).
STEP 4
The electricity bills provided by TNB spanned 24 months between January 2010 and January 2012. However, to comply with the CCM top down approach, the electricity bills were only tabulated for 12 months, averaging between January/February 2010 to January/February 201167. Next, the total of 12 blocks for each PPR low-cost housing project is tabulated (refer Table 6.6) for sample of electricity consumption for total of 12 blocks in PPR Intan Baiduri low-cost housing project).
Following that, the total for each PPR low-cost housing project is tabulated. PPR Beringin and PPR Intan Baiduri’s sample size of 181 and 202 units total electricity consumption was calculated at 480,616 kWh and 513,065 kWh respectively, for the determined 12 months period (refer Table 6.7 and Table 6.8). The summary data (refer Table 6.9) are then inputted to STEP 4 of the bottom-up approach (refer Appendix 6.2b), which presented the GHG emission total of 297 MtCO2e and 317 MtCO2e for PPR Beringin and PPR Intan Baiduri respectively (refer Table 6.10)
67 Due to the different collection dates on the electricity bills, it is more appropriate to analyze the bills according to the number of months, instead of specific months of the year as specified in the CCM’s top-down approach.
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Monthly Electricity Consumption (kWh) Date \ Unit Monthly Total Level (kWh) F2 F1 E2 E1 D2 D1 C2 C1 B2 B1 A2 A1
Month 1 3,309 3,431 4,100 3,796 3,313 2,886 3,508 3,587 2,791 3,493 2,087 3,057 39,358
Month 2 3,614 4,546 4,147 4,411 3,191 2,920 5,314 3,255 2,467 3,368 2,363 2,785 42,381
Month 3 3,462 3,690 3,551 4,292 2,946 2,698 3,322 2,797 1,996 2,755 2,172 2,528 36,209
Month 4 6,168 4,628 4,000 5,628 3,465 3,645 4,467 3,523 3,239 3,901 2,860 3,121 48,645
Month 5 3,733 3,998 3,768 4,087 2,797 3,028 3,874 2,773 2,402 3,174 2,705 2,811 39,150
Month 6 3,671 4,303 4,814 4,265 3,429 2,595 3,273 2,882 1,863 2,928 2,437 2,641 39,101
Month 7 4,255 4,252 3,996 4,151 3,484 3,091 4,118 3,052 2,340 3,435 2,859 3,323 42,356
Month 8 3,902 4,247 5,724 3,729 3,895 3,133 4,040 3,361 2,446 4,084 2,671 3,123 44,355
Month 9 3,781 4,025 3,787 3,963 2,831 2,719 3,697 3,121 2,194 3,202 2,962 3,931 40,213
Month 10 11,285 5,569 4,426 7,654 2,896 2,973 3,643 3,162 2,662 2,888 2,590 2,961 52,709
Month 11 3,534 3,540 2,642 4,994 2,510 2,522 3,289 2,599 2,974 3,325 2,449 2,943 37,321
Month 12 9,308 4,941 2,976 8,844 2,975 2,741 3,620 3,053 2,653 4,824 2,501 2,831 51,267
Total of Year (kWh) 60,022 51,170 47,931 59,814 37,732 34,951 46,165 37,165 30,027 41,377 30,656 36,055 513,065
Table 6.6 Electricity Consumption for Total of 12 Blocks for PPR Intan Baiduri Low-Cost Housing Project
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Monthly Electricity Consumption (kWh) PPR Beringin BLOCK A1 A2 B1 B2 C1 C2 D1 D2 E1 E2 F1 F2 TOTAL
Total per Year (kWh) 46,438 N/A68 38,389 37,961 48,428 50,527 47,619 43,614 43,694 38,784 47,336 37,826 480,616
Average 257.99 N/A 188.18 197.71 237.38 247.68 233.43 242.30 217.46 202.00 232.04 185.42 221.96 (kWh)
No. of Units 15 N/A 17 16 17 17 17 15 17 16 17 17 181
Table 6.7 Total Electricity Consumption for PPR Beringin
Monthly Electricity Consumption (kWh) PPR Intan Baiduri BLOCK A1 A2 B1 B2 C1 C2 D1 D2 E1 E2 F1 F2 TOTAL
Total per Year 36,055 30,656 41,377 30,027 37,165 46,165 34,951 37,732 59,814 47,931 51,170 60,022 513,065 (kWh) Average 176.74 150.27 202.83 156.39 193.57 226.30 171.33 184.96 293.21 242.17 250.83 295.23 211.99 (kWh)
No. of Units 17 17 17 16 16 17 17 17 17 17 17 17 202
Table 6.8 Total Electricity Consumption for PPR Intan Baiduri
68 Electricity consumption data for Block A2 of PPR Beringin was unobtainable at the time of collection at Tenaga Nasional Berhad. Nevertheless, the total number of household units electricity bill collected (383 units) had already met the minimum sample size required (266 units). Therefore, the findings of this case study still generate a statistically sound analysis.
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Monthly Electricity Consumption (kWh) Date \ Unit Level Monthly Total (kWh) PPR Beringin PPR Intan Baiduri
36,312 39,358 75,670 Month 1 35,337 42,381 77,718 Month 2 39,765 36,209 75,974 Month 3 41,320 48,645 89,965 Month 4 38,319 39,150 77,469 Month 5 37,419 39,101 76,520 Month 6 41,702 42,356 84,058 Month 7 39,248 44,355 83,603 Month 8 39,514 40,213 79,727 Month 9 38,070 52,709 90,779 Month 10 45,772 37,321 83,093 Month 11 47,836 51,267 99,103 Month 12
Total of Year (kWh) 480,614 513,065 993,679
Table 6.9 Total Electricity Consumption for Both PPR Low-Cost Housing Projects
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Enter data on electricity used by these buildings Can electricity consumption data be disaggregated by month Default emission factor (kg GHG / kWh): 0.6190755
Grid electricity Green power / renewable
Amount of Custom renewable emission energy that Total GHG factor (kg is generated emissions Use default GHG / per Amount of on-site and (metric Building emission selected green power transferred tonnes Notes / Source
Building name ID factor? Amount Units unit) purchased to the grid CO2e.) of data Kilowatt Averaged PPR Beringin I Yes 480,616.00 hour (kWh) 297.006 through Kilowatt Sample Size of PPR Intan Baiduri T Yes 513,065.00 hour (kWh) 317.059 383 units
614.065
Table 6.10 Data Derived from the Bottom-Up Approach STEP 4 Source: (UNEP-SBCI, 2011)
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STEP 5
Average monthly consumption is calculated at approximately 222 kWh/month and 212 kWh/month for PPR Beringin and PPR Intan Baiduri respectively (also refer Table 6.7 and Table 6.8). Also the total electricity consumption for both PPR Intan Baiduri and PPR Beringin is then calculated at 993,679 kWh (also refer Table 6.9) The next STEP 5 is to calculate the fuel type consumed for the sample size. The disaggregation of fuel type for the sample size follows the top-down approach fuel disaggregation for the *Building Stock (refer Table 6.11). The applicable fuel type disaggregation (i.e. natural gas, oil and diesel) is then inputted as STEP 5 of the CCM bottom-up approach (refer Appendix 6.2c, 6.2d and 6.2e).
Fuel Type Consumed for 2010 Percentage of Fuel Fuel Consumption for Sample Size (GWh) (%) (383 household units) Consumption of 993,681 kWh Hydro 5,227 18.88 % 187,607 kWh Natural Gas 22,337 80.67 % 801,602 kWh Coal - 0 % 0 Oil 6 0.02 % 199 kWh Diesel 119 0.43 % 4,273 kWh Total 27,689 100 % 993,681 kWh
Table 6.11 Fuel Type in Generation Mix for Electricity Produced by TNB and Calculated Fuel Consumption for Sample Size Source: (Energy Commission, 2010)
Finally, the bottom-up summary provides performance metrics that compare the performance of individual buildings, in terms of its energy consumption (kWh/m2/yr or kWh/occupant/yr) and its GHG emission (kgCO2e./m2/yr or kgCO2e./occupant/yr) (UNEP- SBCI, 2010b). PPR Beringin’s energy consumption performance was calculated to be 76 kWh/m2/yr or 962 kWh/occupant/yr, while PPR Intan Baiduri at 73 kWh/m2/yr or 920 kWh/occupant/yr. The GHG emission was calculated at 33 kgCO2e./m2/yr or 415 kgCO2e./occupant/yr for PPR Beringin, and 32 kgCO2e./m2/yr or 397 kgCO2e./occupant/yr for PPR Intan Baiduri (UNEP-SBCI, 2011) (refer Table 6.12). The sample size’s performance is then benchmarked against the *Building Stock performance (refer Table 6.13).
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Individual performance metrics
These tables compare the performance of individual buildings with that of all buildings sampled from the same building category (e.g., a hotel would be compared with all hotels within the sample).
2 2 kWh / m / yr kg CO2e. / m / yr kWh / occupant / yr kg CO2e. / occupant / yr Building name Building ID This building All This building All This building All This building All PPR Beringin I 76 75 33 32 962 940 415 406 PPR Intan Baiduri T 73 75 32 32 920 940 397 406
Table 6.12 Data Summary for Bottom-Up Approach for Sample Size of 383 Household Units Source: (UNEP-SBCI, 2011)
Performance benchmarked against the Whole These tables compare the performance of the entire sample with that of the Whole.
2 2 kWh / m / yr kg CO2e. / m / yr kWh / occupant / yr kg CO2e. / occupant / yr Sample Whole Sample Whole Sample Whole Sample Whole Total residential 75 32 940 406 Single-family residential
Multi-family residential 75 133 32 60 940 1674 406 761 Other residential
Table 6.13 Performance Benchmarks for Sample Size against *Building Stock (or Whole) Source: (UNEP-SBCI, 2011)
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6.3 Analysis of Findings for Both Top-Down and Bottom-Up Approaches
One of the main research aims was to measure energy performance and operational GHG emissions of the selected PPR low-cost housing typology. The CCM provided a comparison between the sample size’s energy performance using the bottom-up approach, against the calculated performance of the *Building Stock using the top-down approach. Analyses of the findings are based on the analytical framework explained in Chapter 4 (reprised as Figure 6.1).
Figure 6.1 Analytical Framework
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6.3.1 Energy Performance Analysis
The bottom-up data for the sample size’s energy consumption performance averages 75 kWh/m2/yr, represented by 76 kWh/m2/yr for PPR Beringin and 73 kWh/m2/yr for PPR Intan Baiduri. In comparison, using the top-down approach, the *Building Stock’s energy consumption was calculated at 133 kWh/m2/yr. In terms of energy performance by occupant, the sample size was calculated at 940 kWh/occupant/yr, and the *Building Stock was calculated at 1674 kWh/occupant/yr (refer Table 6.14).
Energy Consumption Performance Metrics kWh/m2/yr kWh/occupant/yr kWh/household/yr
Top-Down Approach 133 1674 8,370
Bottom-Up Approach 75 940 4,700
Noordin (2012)69 - - 3,012
TNB (1999) cited in - - 2,754 Tang (2005)
Sinha & Jenkins (2012) - - 2,700
IEA (2011) - - 550
WEC (2010) - - 3,500
Table 6.14 Comparison between Case Study Findings and Other Household Energy Consumption Benchmarks Source: (Economidou et al., 2011; IEA, 2011d; Tang, 2005; UNEP-SBCI, 2011; Urge-Vorsatz et al., 2012a; WBCSD, 2009; WEC, 2010)
This can also be calculated in terms of energy performance per household of five (5) persons, in order to be compared with the benchmarks set in Chapter 4. This provided an estimate of 8,370 kWh/household/year for the top-down approach, and 4,700 kWh/household/year for the bottom-up approach. Estimated average electricity consumption for Malaysian low-income households in urban areas is calculated at approximately 2,754 kWh/household/year70 (refer Table 6.14) (TNB, 1999 cited in Tang, 2005). Both top-down and bottom-up findings clearly indicate that current electricity
69 It must be noted that the average data household GHG emission used in this report is based on a household unit consisting of 4.31 persons, according to the 2010 Population and Housing Census (Department of Statistic Malaysia, 2011; Noordin, 2012). 70 The TNB (1999) calculation of Malaysia’s urban household average electricity consumption in this report did not specify the average household occupants (Tang, 2005).
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Additionally, the research’s top-down and bottom-up findings exceeds international benchmarks set by the World Energy Council (WEC) and the International Energy Agency’s (IEA) estimation of 3,500 kWh/household/year and 550 kWh/household/year respectively (also refer Table 6.14) (IEA, 2011d; WEC, 2010). Sinha & Jenkins (2012) estimated world average household electricity consumption at 225 kWh/month, after dividing the world total electricity consumption for the residential sector (IEA, 2011e) by the world total number of households (Dorling, 2007). This approximate calculation yields a world average of 2,700 kWh/household/year (Sinha & Jenkins, 2012).
The case study findings evidently indicate a level of electricity consumption for low- income households in Malaysia that surpasses international benchmarks by a considerable amount. Nevertheless, it must be acknowledged that all these estimates are based on different methodologies and systems boundaries, which renders a comprehensive and equivalent comparison difficult, but worth mentioning as an approximate benchmark.
6.3.2 Operational GHG Emission Analysis
The CCM calculated operational GHG emissions from electricity bills for the building sample using the bottom-up approach (UNEP-SBCI, 2011). The estimate for the GHG emissions for the sample was 32 kgCO2e./m2/yr, or 406 kgCO2e./occupant/yr (UNEP- SBCI, 2011). The top-down approach calculated the *Building Stock GHG emissions at
60 kgCO2e./m2/yr, or 761 kgCO2e./occupant/yr (UNEP-SBCI, 2011).
These findings can further be calculated in terms of energy performance per household of five persons, in order to be compared against the national average and a few international estimates. Therefore, the top-down and bottom-up findings provides an estimate of 3,805 kgCO2e./household/yr and 2,030 kgCO2e./household/yr, respectively. There is a significant gap between the findings presented by the top-down and bottom up approaches, and is further are discussed in the next section
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GHG Emission Sources of
Performance Metrics 2 kgCO2e/m /yr kgCO2e/occupant/yr kgCO2e/household/yr
Top-Down Approach 60 761 3,805
Bottom-Up Approach 32 406 2,030
Noordin (2012)71 - - 2,064 (Malaysia)
WBCSD (2009)72 38 (France) - -
UNEP-SBCI73 33 2,377 -
World Bank (2012) - 4,70074 (World) -
Table 6.15 Comparison of Findings with other Residential Buildings Emissions Benchmarks Source: (Noordin, 2012; WBCSD, 2009; World Bank, 2012)
The case study findings were then compared with other emissions benchmark (refer Table 6.15). The World Bank (2012) estimated the world average for 2009 was approximately 4.7 metric tonnes of carbon dioxide (Mt) per capita75 (World Bank, 2012), while the World Business Council for Sustainable Development (WBCSD) estimated the average French household produces annually 38 kgCO2e./m2/yr (WBCSD, 2009). It must be noted that these comparisons are based on different methodologies and system boundaries, but worth mentioning as an approximate benchmark. This will be further discussed as one of the limitations of the findings.
Noordin’s (2012) calculation of Malaysia’s national household GHG emissions was approximately 2,064 kgCO2e/household/yr, while comparing with the bottom-up findings from this research is only slightly lower (also refer Table 6.15). The top-down estimates from this research are approximately 54% higher than the national household GHG
71 The average data household GHG emission used in this report is for Malaysia and based on a household unit consisting of 4.31 persons, according to the 2010 Population and Housing Census (Department of Statistic Malaysia, 2011; Noordin, 2012). 72 This estimation is based on a single family house in France, an assumes an average of 2.4 persons per household in Western Europe (WBCSD, 2009). 73 The UNEP-SBCI (2010d) data is based on a case study that is on a city-level scale (City A) with an area of 176 km2 and occupancy of 3,700,000 people, and was analyzed using a top-down approach (UNEP-SBCI, 2010d). 74 Converted from 4.7 MtCO2 to 4,700 kgCO2 (World Wide Metric, 2010). 75 The carbon emissions here are defined as “those stemming from the burning of fossil fuels and the manufacture of cement. They include carbon dioxide produced during consumption of solid, liquid, and gas fuels and gas flaring” (World Bank, 2012, para. 1). It must also be noted that is benchmark is not specific to household emissions, but an average per capita emissions of countries.
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6.3.3 Discrepancy between Top-Down and Bottom-Up Analysis
The case study findings contributed to the development of a project-specific energy and GHG performance baseline for government-funded public PPR low-cost housing projects. Therefore, the case study demonstrated that generating energy and GHG performance baselines for the Malaysian building stock is feasible using the UNEP-SBCI CCM, despite its weaknesses. Limitations were encountered in adopting the UNEP-SBCI’s Common Carbon Metric’s top-down and bottom-up approaches simultaneously.
The CCM’s main function is to generate baselines of energy consumption and GHG emissions through two methods of analysis, which is established through different sets of data. The top-down approach’s data set is broad and compiled for either regional, city of national level, as compared to the more narrow focus of the bottom-up approach that gains data from individual buildings or groups of buildings. While adopting both the top- down and bottom-up approaches is intended to be a validation measure, it doesn’t appear to work unless more detailed and more accurate disaggregation of national energy data, based on city (or region/state) and building typology can be gained.
Consequently, the different values of the top-down and bottom-up approaches are mainly due to the disparate systems boundaries applied. The CCM’s main weakness is its analytical vulnerability and dependency on availability and access to data. Additionally, there is no control for differences in systems boundaries between the top- down and bottom-up approaches. The effects of the different systems boundary between the top-down and bottom-up approaches therefore requires further discussion.
The case findings presented the analysis of both the top-down and bottom-up approaches that provided two different outcomes. The reason for the difference can be sought in the slight dissimilarity in each approach’s system boundary. This is further explained in Table 6.16, as well as in the comments on the elements comprising the system boundaries.
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Case Study Protocol
Systems Boundary Top-Down (Population Size) Bottom-Up (Sample Size)
Energy National electricity consumption Utilized electricity bills from the provided by National Energy Balance National Grid, through TNB comprised of TNB, SESB, SEB and IPPs
Fuel Hydro (18.88%) Hydro (18.88%) Natural Gas (80.67%) Natural Gas (80.67%) Coal (0%) Coal (0%) Oil (0.02%) Oil (0.02%) Diesel (0.43%) Diesel (0.43%)
Location Kuala Lumpur Kepong District of Kuala Lumpur
Building Typology All existing PPR low-cost housing 2 PPR low-cost housing projects in projects in KL Kepong, KL
Number of Household Units 27,102 units 383 units
Period of Analysis (or Building Life- Operational phase Operational phase Cycle)
Time Frame 1 year (2010) 1 year (2010)
Table 6.16 Systems Boundaries for CCM’s Top-Down and Bottom-Up Approaches
Energy
In reference to the research case study, a city level estimate was needed for the top- down approach. However since energy data was unavailable at a city level, the National Energy Balance data was used instead, where national electricity consumption data was compiled based on many different energy utility provider companies. The National Energy Balance data is compiled through energy performance data provided by energy utility provider companies in Malaysia (Tenaga Nasional Berhad (TNB), Sabah Electricity Sdn Bhd (SESB), Sarawak Energy Berhad (SEB), and independent power producers (IPPs)) (Energy Commission, 2011b). Additionally, the electricity consumption data from the National Energy Balance (Energy Commission, 2011c) could include electricity loads for common areas and services such as lifts and public lighting.
In contrast, the bottom-up approach, the energy boundary comprised of householder electricity bills provided by TNB, which was the main provider for Peninsula Malaysia and the district of Kepong. The type of fuel used to generate electricity could be inconsistent between TNB and the other companies. However, in reference to this case study, both top-down and bottom-up approaches used the fuel type provided by National
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Energy Balance in the type of fuels used to generate electricity. Additionally, the bottom-up approach only measured electricity consumed specifically within the compound of each individual household unit or apartment (also refer Table 6.16). The difference in the energy boundary between the top-down and bottom-up approaches could explain the discrepancy between the two analyses.
Location
The National Energy Balance data is pro-rated national electricity consumption data based on all the states in Malaysia and the top-down approach looked at Kuala Lumpur city as its location boundary. Whereas the case study had a specific location boundary consisting of the district of Kepong, in the city of Kuala Lumpur. This difference (not an inconsistency) could also have affected the top-down and bottom-up analyses (also refer Table 6.16).
Building Typology
Apart from the ‘energy and location systems boundary’, dissimilarities were also encountered through the ‘building typology systems boundary’. The residential and commercial sector defined in the National Energy Balance “not only refers to energy used within households and commercial establishments but includes government buildings and institutions” (Energy Commission, 2011b, p. 74). Additionally, the top- down approach measures the energy and emissions performance of the identified population (or *Building Stock), and calculations were based on all existing PPR low- cost housing units in Kuala Lumpur for 2010 (a total of 27,102 household units).
In comparison, the case study focused on electricity consumption data of a very specific disaggregated typology within the residential sector, the public PPR low-cost housing typology. Additionally the bottom-up calculations were based on two specific PPR low- cost housing projects in Kepong, PPR Beringin and PPR Intan Baiduri, with a total of 383 household units, with an average household size of 5 persons per unit and a net floor area of 63m2 per unit (also refer Table 6.16).
Period of Analysis (or Building Life-Cycle)
The top-down approach analysed data for the year 2010 using the National Energy Balance and Annual Property Stock Report, while the bottom-up analysed utilized
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6.4 Summary
This chapter has presented an estimate performance metrics for the PPR low-cost housing typology in Kuala Lumpur, which comprised of a population size of 27,102 units (or *Building Stock), using the top-down approach. The research project also collected electricity bills for 383 household units (known as sample) to be tabulated in the CCM using the bottom-up approach. The summary of findings is compiled together in Table 6.17. These findings are then also inserted into the analytical framework diagram for a more visual representation (refer Figure 6.2).
Energy Consumption GHG Emission Performance
Metrics kWh/ kWh/ kWh/ kgCO2e/ kgCO2e/ kgCO2e/ m2/yr occupant/yr household/yr m2/yr occupant/yr household/yr
Top-Down 133 1674 8,370 60 761 3,805 Approach
Bottom-Up 75 940 4,700 32 406 2,030 Approach
Table 6.17 Summary of Analysis from Top-Down and Bottom-Up Approaches
This chapter has also presented the analysis of findings from the fieldwork conducted to investigate measured energy performance and GHG emissions from the operation of two PPR low-cost housing projects in Kuala Lumpur. Adopting the UNEP-SBCI’s Common Carbon Metric in the specific systems boundary, the findings presented show that the GHG emissions of the sample (383 units) was lower in comparison with the *Building Stock (27,102 units), in terms of m2 and per occupant.
The *Building Stock’s GHG emissions was calculated at 60 kgCO2e./m2/yr or 761 kgCO2e./occupant/yr, using the top-down approach. The CCM calculated the sample size
GHG emission at 32 kgCO2e./m2/yr or 406 kgCO2e./occupant/yr, using the bottom-up approach. Additionally, the number of electricity bills collected surpassed the minimum sample size of household units required for the known population and it is believed that a statistically sound analysis was carried out.
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Figure 6.2 Research Findings Integrated into the Analytic Framework
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This gap in analysis between the top-down and bottom-up approaches, which calculated the *Building Stock and sample size respectively, is likely due to dissimilarities within the systems boundary adopted. As discussed in this section, there are dissimilarities between within most of the systems boundary adopted, for the top-down and bottom-up approaches. The only systems boundary that remain parallel between the two approaches are the ‘fuel type’ and ‘time frame’ of the building life-cycle, which is a one (1) year duration of building the operational phase. A gap in analysis is often encountered when using both the top-down and bottom-up analysis simultaneously, and it is recommended for a hybrid approach in order to resolve this gap (Ellis & Bosi, 2000). Further research should be done to investigate in depth the discrepancy of findings using the top-down and bottom-up approach, in order to achieve a more consistent analysis and accurate baseline.
Equally, this also indicates that an aggregation or to average the top-down and bottom- up findings is not advisable when there are dissimilarities within the systems boundary of both approaches. As a result, this research will report the *Building Stock and sample size energy consumption and GHG emissions according to both the top-down and bottom-up analysis. However, emphasis should be given to the bottom-up approach analysis in this research’s contribution to knowledge, as it is based on empirical data collected from the National Energy Provider Company, Tenaga Nasional Berhad. In the next chapter, findings from the survey questionnaire are presented and analysed.
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Chapter 7: Patterns & Affordability of Electricity Consumption: Survey Results
7.1 Introduction
This Chapter presents the survey findings on operational affordability and end-use electricity consumption patterns for low-income households in two PPR low-cost housing projects in the district of Kepong, Kuala Lumpur. The survey was conducted over approximately 11 weeks, between December 2011 and March 2012. The time frame chosen was often during weekdays, between 11.00am to 1.00pm or between 2.00pm to 5.00pm to target persons who are usually home during the day. This time frame helped ensure cooperation and a high response rate, and to avoid disturbing households during the weekend. Furthermore, to conduct the fieldwork within the allocated time, a face-to- face method helps to ensure high percentage of responses, which is highly dependent on the availability of households who are willing to participate, and in dealing with different levels of literacy.
Surpassing the minimum sample size, a total of 281 household units were interviewed - 129 household units in PPR Beringin and 152 household units in PPR Intan Baiduri. Accessibility to respondents in this context was narrowed down to household units which had their front door open. An introduction to the research and the survey was given to the householder and depending on their willingness to participate, the survey was conducted. Analysis and discussion of the findings are presented interchangeably in this Chapter.
7.2 Basic Demographics
The total number of occupants was calculated at 1,495 person, within the 281 household units surveyed, with 691 persons from PPR Beringin and 804 persons from PPR Intan Baiduri. The total number of persons living in the 281 households surveyed was calculated in Microsoft Excel, which then enables the mode of household to be calculated. This consequently provided a combined average of 5.33 persons per household, and a mode of five persons living in each household (refer Table 7.1).
Defining the mode of a household is the main finding of this section of the questionnaire since it is used in the CCM’s calculation to provide estimates of total occupants in its
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No. of Average No. Mode of No. of Occupants of Occupants Occupant Address Households Renter Owner Others Living in Living in Living in Surveyed Household Household Household (persons) (persons) (persons) PPR Beringin 129 99 6 24 691 5.36 5 PPR Intan 152 139 8 5 804 5.29 5 Baiduri 281 238 14 29 Total 1,495 5.33 5 (100%) (85%) (5%) (10%)
Table 7.1 Basic Demographic Findings
7.3 Average End-use Operating Time of Electrical Appliances
The data of average daily electricity consumption was categorized based on different types of electrical appliance and placed into an Excel worksheet. Analysis of the average daily operating time of electrical appliances from the sample size helped identify which type of electrical appliance requires more consideration. The type of end-use electrical appliances covered in the survey are reiterated here:
1) Artificial lighting; 2) Artificial cooling; 3) Hot water systems; 4) Refrigeration; 5) Entertainment and technology; 6) Cooking and kitchenware; and 7) Clothes washing.
Consequently, these electrical appliances are analysed either in terms of building design, or electrical appliances efficiency recommendations. The findings presented are the combined findings of both PPR Intan Baiduri and PPR Beringin low-cost housing projects.
76 ‘Other’ category could imply that the occupant was in transit, or rent is provided by a third party, i.e. developer’s company or charitable organization.
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7.3.1 Artificial Lighting Appliances
Artificial lighting appliances in this context means any electrical appliance used to provide light in the household unit, such as incandescent ceiling lights, and fluorescent lamps. As the focus of this research was to investigate end-use daily consumption of electrical appliances and not ownership of appliances, type of lighting appliance is not distinguished. The survey findings show that 28.7% of total sample size utilized artificial lighting appliances for approximately 4 to 5 hours daily. Approximately 25% of households utilizes artificial lighting appliances for an average operating time between 3 to 4 hours daily. Another 14.8% of households utilizes artificial lighting appliances for an average operating time between 5 to 6 hours daily. The details appear in Table 7.2 and Figure 7.1.
Average Operating Time (Hours/Day) Types of Electrical Total Appliance Above 24 (%) N/A77 0 -1 1 - 2 2 - 3 3 - 4 4 - 5 5 - 6 6 hours
Artificial Lighting 0.0 0.0 0.3 2.8 25.1 28.7 14.8 23.3 5.1 100 (%)
Combined Total 3.1 68.6 23.3 5.1 100 (%)
Table 7.2 Average Household Daily Artificial Lighting Operating Hour (n=281)
Percentage of Household (%) Average Operating Time Average for Artificial Lighting N/A 3% 0-1 h 5% 1-2 h 23% 25% 2-3 h 3-4 h 4-5 h 15% 5-6 h 29% Above 6 h 24 hs
Figure 7.1 Daily Average Operating Time for Artificial Lighting
77 N/A in this research context means either the electrical appliance is unavailable for the household unit, or the household units do not use such electrical appliances on a daily basis, and is applied throughout the end- use operating time of electrical appliances findings.
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The survey findings also calculated an approximate of 23% of household’s artificial lighting operating time was more than 6 hours daily. There is also a small percentage of households, (5.1%) that have some lighting appliance switched on for 24 hours per day. On average however, approximately 69% of households’ operating time for lighting appliances is between 3 to 6 hours daily (also refer Table 7.2).
The IEA’s compilation of average household lighting operating time for countries such as the United Kingdom, USA and Japan calculated their average lighting operating time of approximately 1.60, 1.92 and 3.38 hours per day, respectively (refer Table 7.3) (IEA, 2006). Additionally, the average overall operating time for lighting appliances is calculated at 1.62 hours per day78. However, it is understood that this estimate is limited to the OECD countries provided in Table 7.3.
Table 7.3 Comparison of Average Household Lighting Consumption Source: (IEA, 2006)
78 The average operating time was calculated by deriving the total operating time by the number of countries provided in the list. However, it is understood that this estimation is not the world average consumption, and limited to the OECD countries provided on the list.
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Additionally, according to previous studies by Tang (2005) and Saidur, et al. (2007), the Malaysian household utilizes approximately 4 hours/day and 9 hours/day respectively, for artificial lighting (Saidur et al., 2007; Tang, 2005). The current research indicated that 69% of households’ operating time for artificial lighting is between 3 and 6 hours daily. The findings suggest that the sample size’s average operating time is higher than the calculated average of OECD countries in Table 7.3 and the estimated average Malaysian household of 4 hours/day by Tang (2005). Therefore, strategies for energy efficient lighting should be recommended to help reduce the energy consumption of households in Malaysia considering the long operating hours spent using artificial lighting appliances.
7.3.2 Artificial Cooling Appliances
Artificial (or active) cooling appliances is this context means any mechanical appliances, which its primary function is for ventilation or cooling purposes such as ceiling fan, standing fan, and air-conditioners. The research objective was to investigate end-use daily operating time of electrical appliances and not explicitly investigate ownership of appliances. However, the case study findings presented a combined total of 18 household units that had air-conditioning systems installed. This represents only a small percentage of 6.4% of households surveyed, therefore artificial cooling appliances in this research mainly refers to mechanical fans such as ceiling or standing fans.
The survey findings calculated approximately 47% of households utilize electrical appliances for artificial cooling in an estimated duration of 24 hours per day79 (refer Table 7.4 and Figure 7.2). This implies that for these households, someone is always home. Another 47% of households utilizes approximately six hours daily average for artificial cooling needs.
In comparison with research done by Tang (2005), the average daily operating time for artificial cooling appliances is calculated at 11.7 hours per day (Tang, 2005), while Saidur, et al. (2007) reported an average of 16.7 hours per day for artificial cooling appliances. The remaining 5.8% of households spend less than six (6) hours daily of operating time for artificial cooling appliances. A small percentage of 0.4% of households either does not own or did not install any artificial cooling appliances.
79 As per conversations with the respondents, these artificial cooling appliances are only switched off when all of the occupants leave the unit.
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It is acknowledged that the gap in the range of operating time ‘above 6 hours’ to ‘24 hours’ per day is considerably large, at 18 hours difference. This operating time gap in an oversight in the survey questionnaire design, which did not anticipate end-use operating time for electrical appliance to go beyond 6 hours per day and/or for 24 hours per day. This consequently impairs a comparative analysis with previous research done by Saidur, et al. (2007) and Tang (2005), but an average benchmark can still be predicted through the survey findings.
Types of Average Operating Time (Hours/Day) Total Electrical Above 24 (%) Appliance N/A 0 -1 1 - 2 2 - 3 3 - 4 4 - 5 5 - 6 6 hours Artificial Cooling 0.4 0.7 0.0 0.4 1.0 1.0 2.7 46.6 46.9 100 (%) Combined Total 0.4 5.8 46.6 46.9 100 (%)
Table 7.4 Average Household Daily Artificial Cooling Operating Hour (n=281)
Percentage of Household (%) Average Operating Time Average for Artificial Cooling 1% 1% N/A 1% 3% 0-1 h 1-2 h 47% 2-3 h 3-4 h 47% 4-5 h 5-6 h Above 6 h 24 hs
Figure 7.2 Daily Average Operating Time for Artificial Cooling
This also indicates that the majority of households (94%) spend approximately between 6 (47%) to 24 hours (47%) daily of operating time for cooling appliances. This suggests that natural ventilation is seriously inadequate for coping with average daily temperature and humidity. This could be either due to the design and layout of the housing project and its individual units, or the site location and/or orientation. This suggests an area of research that could be conducted, to investigate the impact of such climatic design on consumption of artificial cooling appliances.
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7.3.3 Hot Water Systems
Hot water systems are defined in this research as an electrical appliance installed to provide hot water. According to the findings, a large majority of households, 76% of the total sample size does not have hot water systems installed in their individual units (refer Table 7.5 and Figure 7.3). Approximately 213 household units of the total sample size of 281 units did not have hot water systems installed in their individual units. The remaining 24% household units utilizes their hot water systems for less than 3 hours daily, where approximately 5% utilizes it for less than 1 hour daily; 14% use it between 1 to 2 hours daily; and the balance of 5% use it between 2 to 3 hours daily (also refer Table 7.5).
Tang (2005) estimates that the average household utilizes 1 hour/day for water heater systems, and that only an approximate of 29% Malaysian households had installed the system (Tang, 2005). Similarly, Saidur, et al. (2007) reported the average operating time for water heater at 0.7 hour/day. This suggests that hot water systems do not make a significant contribution to end-use electricity consumption for the *Building Stock. This could be due to climatic conditions of Malaysia, or due to the demographics of low- income household units where hot water is not a priority. This was also noted while conducting the survey as many of the households interviewed said they did not require hot water for their showers as the climate was warm and humid all year long.
Types of Average Operating Time (Hours/Day) Total Electrical Above 24 (%) Appliance N/A 0 -1 1 - 2 2 - 3 3 - 4 4 - 5 5 - 6 6 hours Hot Water 76.3 4.8 13.7 5.1 0.0 0.0 0.0 0.0 0.0 100 System (%) Combined Total 76.3 23.6% 0 100 (%)
Table 7.5 Average Household Daily Hot Water Systems Operating Hour (n=281)
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Percentage of Household (%) Average Operating Time Average for Hot Water System N/A 0-1 h 5% 14% 1-2 h 5% 2-3 h 3-4 h 4-5 h 76% 5-6 h Above 6 h 24 hs
Figure 7.3 Daily Average Operating Time for Hot Water Systems
7.3.4 Refrigeration
Refrigeration in this research is defined as refrigerators. The survey findings indicated that 98% of households surveyed had refrigerators in their individual units, with 24 hour/day in operation time (refer Table 7.6 and Figure 7.4). This represents 275 households units with refrigerators and suggests that refrigerators are significant in the ensemble of electrical appliances for individual households. The remaining 2% of households surveyed which were not equipped with refrigerators either could not afford refrigerators due to financial constraints, or was a single occupant household whom did not consider it a necessity.
Types of Average Operating Time (Hours/Day) Total Electrical Above 24 (%) Appliance N/A 0 -1 1 - 2 2 - 3 3 - 4 4 - 5 5 - 6 6 hours
Refrigeration (%) 2.2 0.0 0.0 0.0 0.0 0.0 0.0 0.4 97.4 100
Table 7.6 Average Household Daily Refrigeration Operating Hour (n=281)
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Percentage of Household (%) Average Operating Time Average for Refrigeration N/A 2% 0-1 h 1-2 h 2-3 h 3-4 h 4-5 h 98% 5-6 h Above 6 h 24 hs
Figure 7.4 Daily Average Operating Time for Refrigeration
Saidur, et al. (2007) on the other hand only reported an average of 8.1 hours/day of operating time for refrigeration appliances. However, according to Tang’s (2005) study, the average Malaysian household operating time for refrigerators was 24 hours/day, but only estimated 58% of households owned refrigerators (Tang, 2005). The survey findings, indicate a significant increase in refrigerator ownership between 2005 and today. Recommendations for energy efficient refrigerators could significantly reduce household electricity consumption, based on its 24 hour daily operating time and its high percentage of ownership.
7.3.5 Entertainment and Technology Appliances
Individual electrical appliances that fall under this category are such as television, computer, laptop, and both video and music players. The findings indicated that approximately 98.9% of households had electrical appliances under this category available in their households. Approximately 1% of households surveyed suggested they do not own or utilize any electrical appliances within this category on a daily basis.
A majority of households spend more than 6 hours consuming electrical appliances related to entertainment and technology, represented by 54% of the household units surveyed. The remaining 45% of households spend less than 6 hours daily for electrical appliance under this category, with 9% consuming approximately between 1 to 3 hours/day and 36% consuming approximately between 3 to 6 hours/day (refer Table 7.7 and Figure 7.5). There was also a small percentage of households (0.4%) that utilize electrical appliances under this category 24 hours daily (also refer Table 7.7). Based on
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Types of Average Operating Time (Hours/Day) Total Electrical Above 24 (%) Appliance N/A 0 -1 1 - 2 2 - 3 3 - 4 4 - 5 5 - 6 6 hours Entertainment & 1.1 0.3 2.2 6.3 10.7 14.6 10.5 54.1 0.4 100 Technology (%) Combine Total 1.1 8.8 35.8 54.1 0.4 100 (%)
Table 7.7 Average Household Daily Entertainment and Technology Operating Hour (n=281)
Percentage of Household (%) Average Operating Time Average for Entertainment & Technology 1% 2% N/A 0-1 h 6% 11% 1-2 h 2-3 h 54% 15% 3-4 h 4-5 h 11% 5-6 h Above 6 h 24 hs
Figure 7.5 Daily Average Operating Time for Entertainment and Technology
According to Tang (2005), 2.9 hours/day was the combined average operating time by electrical appliances under this category, i.e. television, radio and computers in households (Tang, 2005). However, according to Saidur, et al. (2007), they reported the average operating time for appliances under this category was approximately 17.6 hours/day. In comparison with the research findings, the average household has a much higher operating time for entertainment and technology is much higher the Tang’s (2005) average, but lower than Saidur, et al. (2007). This presents an opportunity to design more energy efficient entertainment and technology appliances, especially television, in reducing the direct energy consumption of households.
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7.3.6 Cooking and Kitchen Ware Appliances
The individual electrical appliances that fall under this category include the electric stove, electric oven, microwave, rice cooker, and water dispenser. Approximately 20% of households suggested that they did not own or utilized any of the individual electrical appliances under this category on a daily basis. Conversely, there was a small percentage of households (2.1%) that suggested that they utilize certain types of cooking and kitchenware appliances 24 hours daily, which was mainly the water dispenser system. Another very small percentage (0.3%) of households spend more than 6 hours daily using cooking and kitchenware appliances (refer Table 6.8 and Figure 7.6). This could be due to the type of work the household occupant does, which occasionally includes food catering and preparation.
The remaining majority of households (77%) spend less than 4 hours daily on cooking and kitchenware appliances. Forty eight per cent spend less than 1 hour daily; 21% between 1 to 2 hours daily; 6% between 2 to 3 hours; and the balance of 2% spends between 3 to 4 hours daily (also refer Table 6.8). Therefore a combined average of 75.4% of households consumes less than 3 hours/day of electricity for cooking and kitchenware appliances. This fluctuation of average daily consumption of cooking and kitchenware appliances could also be linked with the number of persons living in each household.
Types of Average Operating Time (Hours/Day) Total Electrical Above 24 (%) Appliances N/A 0 -1 1 - 2 2 - 3 3 - 4 4 - 5 5 - 6 6 hours Cooking & 20.3 48.4 21.4 5.6 1.9 0.0 0.0 0.3 2.1 100 Kitchen Ware (%) Combined Total 20.3 75.4 1.9 0.0 0.0 0.3 2.1 100 (%)
Table 7.8 Average Household Daily Cooking and Kitchen Ware Operating Hour (n=281)
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Percentage of Household (%) Average Operating Time Average for Cooking & Kitchenware
2% 2% N/A 0-1 h 6% 20% 1-2 h 21% 2-3 h 3-4 h 4-5 h 49% 5-6 h Above 6 h 24 hs
Figure 7.6 Daily Average Operating Time for Cooking and Kitchenware
Tang (2005) estimated that the rice cooker was utilized for approximately 6.9 hours/day in an average household, and no other cooking or kitchenware appliances were included in his research. Meanwhile, Saidur, et al.’s (2007) report included many other cooking and kitchen ware appliances such as rice cooker, blender, toaster, kettle, microwave and electric water filter. Saidur, et al.’s (2007) reported an average of 1.8 hours/day of operating time for the listed electrical appliances in this category.
However, the research findings do suggest that electrical appliances that fall under cooking and kitchenware do not seem to be a significant form of end-use electricity consumption, based on the majority percentage of households consuming less than 4 hours daily. A number of households (20.3%) do not utilize any electrical appliance under this category on a daily basis, and the majority of households (48.4%%) are consuming less than 1 hour daily.
7.3.7 Clothes Washing Appliances
The two main electrical appliances in this category are washing machines and clothes dryers. However, it must be noted that considering Malaysia’s climatic conditions, clothes drying machines are not widely used. Tang (2005) estimated only 4% of Malaysia households had ownership of a clothes dryer (Tang, 2005). The survey findings show that approximately 15% of households surveyed were not equipped with clothes washing appliances (refer Table 7.9 and Figure 7.7).
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Types of Average Operating Time (Hours/Day) Total Electrical Above 24 (%) Appliances N/A 0 -1 1 - 2 2 - 3 3 - 4 4 - 5 5 - 6 6 hours Clothes Washing 14.7 65.5 16.4 3.2 0.0 0.3 0.0 0.0 0.0 100 (%) Combined Total 14.7 65.5 19.6 0.0 0.3 0.0 0.0 0.0 100 (%)
Table 7.9 Average Household Daily Clothes Washing Operating Hour (n=281)
Percentage of Household (%) Average Operating Time Average for Clothes Washing 3% N/A 0-1 h 16% 15% 1-2 h 2-3 h 3-4 h 4-5 h 66% 5-6 h Above 6 h 24 hs
Figure 7.7 Daily Average Operating Time for Clothes Washing
Tang (2005) estimated that 29% of Malaysian households had ownership of washing machines, and spent approximately 0.8 hours/day operating them (Tang, 2005). Similarly, Saidur, et al. (2007) reported the average operating time for clothes washing appliances (including washing machines and dryer) was approximately 1 hour/day. In comparison with the survey findings, the average operating time was found to be less than 1 hour/day (66% of households). Therefore this comparison does not present a significant difference in the average operating time.
Through the questionnaire, it was found that the daily operating time for clothes washing is dependent on the demographics of each household, in terms of total number of persons, the occupation of the residents, and their age group. It also suggested that the average number of hours spent clothes washing is correlated with bigger households, in terms of persons living per household, and the number of young children living per household.
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7.3.8 Summary of End-Use Electricity Consumption
In summary, the findings highlight the type of electrical appliance that play a significant part in the average household in terms of its daily consumption (refer Table 7.10). It was found that the two most significant appliances in the average households were refrigerators and cooling appliances. This is based on the high percentage of households that consumed these electrical appliances for 24 hours on a daily basis, that is 47% for cooling appliances, and 97% for refrigerators.
Types of Average Daily Operating Time (Hours/Day) Total Electrical Above 24 (%) Appliance (%) N/A 0 -1 1 - 2 2 - 3 3 - 4 4 - 5 5 - 6 6 hours Artificial 0.0 0.0 0.3 2.8 25.1 28.7 14.8 23.3 5.1 100 Lighting Artificial 0.4 0.7 0.0 0.4 1.0 1.0 2.7 46.6 46.9 100 Cooling Hot Water 76.3 4.8 13.7 5.1 0.0 0.0 0.0 0.0 0.0 100 System
Refrigeration 2.2 0.0 0.0 0.0 0.0 0.0 0.0 0.4 97.4 100
Entertainment 1.1 0.3 2.2 6.3 10.7 14.6 10.5 54.1 0.4 100 & Technology Cooking & 20.3 48.4 21.4 5.6 1.9 0.0 0.0 0.3 2.1 100 Kitchen Ware Clothes 14.7 65.5 16.4 3.2 0.0 0.3 0.0 0.0 0.0 100 Washing
Table 7.10 Average Daily Operating Hour of Electricity for Surveyed Appliances (%) (n=281)
It was also found that a large percentage of households (54%) usually spend more than 6 hours daily using electrical appliances under the entertainment and technology category. The findings also suggest that clothes washing, cooking and kitchenware appliances do not play a significant part in average household daily end-use electricity consumption. It was also made apparent that the majority of households surveyed - 76%- did not own or had not installed a water heating system in their individual units. A summary of findings are also illustrated in Figure 7.8, which also indicates the type of policy intervention that addresses the end-use energy efficiency issues identified.
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Figure 7.8 Analysis of End-Use Consumption Patterns of Electrical Appliance in Operating Time
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Findings for the consumption of electrical appliances provided similar results for both PPRs. For example, approximately 50% of households in PPR Intan Baiduri consumed on average above 6 hours daily for entertainment and technology appliances, while the percentage was at 58% for PPR Beringin. It must also be emphasized that the research objective was to investigate end-use patterns of electricity in the form of daily operating time of electrical appliances. This research is therefore unable to calculate amount of electricity actually consumed for each electrical appliances, which lies beyond the systems boundary of the case study. This is due to the primary objective for this research, which was to investigate end-use patterns of operating hour consumption and operational affordability rather than energy efficiency. The summary of average consumption of electrical appliances for each PPR is provided in Appendix 7.1a and 7.1b.
Findings for both PPRs provided an average percentage of the daily consumption of the electrical appliances (also refer Table 7.10). From the findings, it indicates that approximately 98% of households are equipped with refrigeration, while a large percentage of households are not equipped with hot water systems (approximate 76%) as shown in Table 7.10). This suggests that hot water systems are not a significant part of an average household electrical appliance ensemble. Only 24% of households have installed such hot water systems. Conversely, the findings point out that refrigerators are the most common electrical appliance in the average households, in approximately 97% of households.
Additionally, the findings reveal that artificial cooling electrical appliances play an important role in the average household, with 99.6% of households that have installed cooling appliances such as ceiling fans, standing fans, and/or air-conditioners. However, the case study findings did present 6.4% or 18 household units had air-conditioning systems installed. Therefore, cooling appliances in this context are represented by either ceiling fans or standing fans. The findings were then illustrated as a bar chart for an enhanced visual presentation (refer Figure 7.9). The bar chart (Figure 7.9) displays the percentage of households daily operating time average (hours per day), by the seven (7) categories of electrical appliance type.
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Percentage of Household Daily Operating (Hours/Day) 100
90
80
70
60
50
40
30
20
10
0 Ventilation & Entertainment & Cooking & Lighting Hot Water System Refrigeration Clothes Washing Cooling Technology Kitchenware N/A 0 0.4 76.3 2.2 1.1 20.3 14.7 0-1 h 0 0.7 4.8 0 0.3 48.4 65.5 1-2 h 0.3 0 13.7 0 2.2 21.4 16.4 2-3 h 2.8 0.4 5.1 0 6.3 5.6 3.2 3-4 h 25.1 1 0 0 10.7 1.9 0 4-5 h 28.7 1 0 0 14.6 0 0.3 5-6 h 14.8 2.7 0 0 10.5 0 0 Above 6 h 23.3 46.6 0 0.4 54.1 0.3 0 24 hs 5.1 46.9 0 97.4 0.4 2.1 0
Figure 7.9 Average Daily Consumption of Electrical Appliance (n=281)
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Building control policies that affect the thermal comfort of buildings through design specifications and envelope standards can be recommended in order to reduce electricity consumption for space cooling. These findings can also be compared to Tang (2005)’s and Saidur et al. (2007) analysis. The case study findings are categorized according to the highest percentage of households’ hourly operating time, corresponding to the type of electrical appliances (refer Table 7.11). The comparison seems to suggest the case study findings and Tang’s (2005) analysis for the average Malaysian household, are similar in terms of artificial lighting, refrigeration, and cooking and kitchen ware appliances.
Case Study Findings Tang (2005) Saidur, et al. (2007) Types of Electrical Appliance (Average Operating (Average Operating (Average Operating Time Daily) Time Daily) Time Daily)
Artificial Lighting 4.5 4 8.98
Artificial Cooling 24 11.3 16.7
Hot Water System N/A 1.0 0.7
Refrigeration 24 24 8.11
Entertainment & Above 6 2.9 17.56 Technology
Cooking & Kitchen 0.5 1.0 1.75 Ware
Clothes Washing 0.5 0.8 1.04
Table 7.11 Comparison of Case Study Average Operating Time with Tang (2005) and (Saidur et al., 2007)
However, there is a stark difference in hourly consumption per day for artificial cooling appliances between Tang’s (2005) findings, Saidur et al. (2007) study and the case study findings. Tang (2005) calculated the average household spends approximately 11.3 hours daily using cooling appliances, and Saidur et al. (2007) calculated an average of 16.7 hours daily (Saidur et al., 2007; Tang, 2005). Meanwhile the case study of PPR Beringin and PPR Intan Baiduri found that the majority of household (94%) spends approximately between 6 to 24 hours daily for cooling purposes.
The comparative analysis supports the recommendation that thermal comfort in the average household unit is insufficient if relying solely on passive design features and
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Recommendations for energy efficiency labelling scheme for electrical appliances can be made based on the daily operating time and average electrical appliance energy consumption. In projecting the average energy consumed (in kilowatt – kW) by type of end-use electrical appliance, an average can be calculated based on Tang (2005) and Saidur et al. (2007) findings. The two research findings data are presented Appendix 7.1 and summarized in Table 7.12. Averaging the two research findings, it shows the type of end-use electrical appliance that consumes the highest amount of energy is for hot water system, with an average of 2.35 kW. The second highest energy consuming appliance is for artificial cooling, at approximate 2 kW, and third is cooking and kitchenware appliances at 0.8 kW.
Therefore, based on the average daily operating time findings and the average energy consumed according to type of electrical appliance, there is an urgent need to focus on reducing energy for artificial cooling loads. Adopting energy efficient cooling appliances could assist in reducing energy consumption for cooling loads. The IEA suggests energy efficient cooling technologies80 have the potential to reduce energy consumption by 710 million tonnes oil equivalent (Mtoe) and GHG emissions down to 2 gigatonnes (Gt) by year 2050 (IEA, 2011c).
Tang (2005) Saidur et. al. (2007) Estimate Types of Electrical Average Power Average Power Average Power Appliance (kW) (kW) (kW) Artificial Lighting 0.4 0.1 0.25
Artificial Cooling 2.5 1.45 1.98
80 Cooling in the IEA report refers to “the conditioning of a building’s internal area where desired internal temperatures are being reduced. It can include dehumidification or humidification” (IEA, 2011c, p. 6)
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Hot Water System 2.7 2 2.35
Refrigeration 0.3 0.2 0.25
Entertainment & 0.43 0.2 0.32 Technology
Cooking & Kitchen 0.7 0.9 0.8 Ware
Clothes Washing 2.2 1 1.6
Table 7.12 Average Power Consumed by End-Use Electrical Appliance based on Tang (2005) and (Saidur et al., 2007) Findings
With regards to the tropical and humid conditions year long and mean daily temperature between 26° to 28° Celsius (MNRE, 2011), constant cooling aid is needed to help occupants maintain a comfortable indoor environment. Studies suggest a comfortable indoor temperature should be between 23° and 26° Celsius with a relative humidity level between 50% to 70% for tropical climates (Prianto & Depecker, 2003; Sookchaiya et al., 2010; Yamtraipat et al., 2005). The survey results show that occupants are almost entirely reliant on artificial cooling to maintain their thermal comfort within this range.
Significantly, recommendations to implement energy efficiency building codes, and bioclimatic and/or passive building design, have been proven to reduce energy consumption in many countries. Such policy and strategies could significantly reduce average household energy consumption in Malaysian low-cost housing based on its significant operating time, and help improve thermal comfort for buildings in the tropical climate which requires significant cooling mechanisms.
It is also acknowledged that this survey questionnaire is limited in providing accurate overall energy consumption for households surveyed, in terms of actual amount of electricity consumed. This is counterbalanced by the electricity bills collected from Tenaga Nasional Berhad (TNB), which provided actual amount of electricity consumed for households, in the CCM bottom-up approach. However, the electricity bills collected from TNB were not collated to those household surveyed, to retain anonymity and confidentially of data. Combining the CCM analysis and the survey questionnaire’s
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However limitations of the questionnaire needs to be considered for future research recommendations, such as providing a more specific time range or duration of operating time for the different electrical appliances to provide a more accurate representation of measured energy patterns. Using a simple power meter and cross-referencing it with the household’s electricity bills could address other limitations such as validation of household appliances energy consumption patterns. This type of research should be done periodically and over a long span of time.
7.4 Household Income and Affordability
Housing affordability in this research context is defined as household spending less than 30% for rent or housing repayments, less than 10% for electricity and less than 6% for other utilities, of its monthly household income. Table 7.13 and Figure 7.10 presents the survey questionnaire findings in a table and pie chart format respectively, based on percentage of household according to the household income range. It was found that most households earned between RM 1000 to RM 2500 a month, with a combined average of 60.5% of the sample size. Some 20.2% of households earned between RM 1000 to RM 1500 a month, 23.6% of households between RM 1500 to RM 2000 a month, and 16.7% earned between RM 2000 to RM 2500 a month (also refer Table 7.13).
Between Between Between Between Between Between Between Household Below RM500 RM1000 RM1500 RM2000 RM2500 RM3000 RM3500 Above Total Income RM500 to to to to to to to RM4000 (%) Range RM1000 RM1500 RM2000 RM2500 RM3000 RM3500 RM4000 PPR Beringin 6.2 14.7 24.0 20.9 12.4 8.5 4.7 3.9 4.7 100 (%) PPR Intan Baiduri 5.9 11.8 16.5 26.3 21.1 7.2 5.3 0.0 5.9 100 (%) Average 6.1 13.3 20.2 23.6 16.7 7.9 5.0 1.9 5.3 100 (%) Combined 19.4 60.5 20.1 100 Total (%) Table 7.13 Average Monthly Household Income Summary (%) n=281
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Percentage (%) of Household According to Monthly Income
5% 2% Below RM500 5% 6% 8% 13% Between RM500 to RM1000 Between RM1000 to RM1500 Between RM1500 to RM2000 17% 20% Between RM2000 to RM2500 Between RM2500 to RM3000 24% Between RM3000 to RM3500 Between RM3500 to RM4000 Above RM4000
Figure 7.10 Average Monthly Income by Percentage (%) of Households Surveyed
7.4.1 Monthly Expenditure for Rent/Housing Loan
Therefore, if most households earn between RM 1000 to RM 2500 a month, 30% of this range would be approximately between RM 300 to RM 750 a month (for rent), while 10% of household earnings would provide a range between RM 100 to RM 250 a month for electricity. Additionally, affordability for combined operational costs of electricity and other utilities, is defined as less than 25% of total household income. To simplify, the following is presented (refer Table 7.14 for the exchange rate):
30% of RM 1000 to RM 2500 = RM 300 to RM 750 for rent 10% of RM 1000 to RM 2500 = RM 100 to RM 250 for electricity 6% of RM 1000 to RM 2500 = RM 60 to RM 150 for other utilities
The majority of households spend an average of RM 100 to RM 150 monthly for rent or housing loan repayments or 88% of total households surveyed. This reflects the low monthly rental set by the Ministry of Housing and Local Government, at RM 124 per month. If the average household is assumed to earn approximately RM 1500 a month (middle range between RM 1000 to RM 2000) and is compared with the standardized rent of RM 124 a month, only 8.3% of monthly household income is spent on rent. However, there was also a percentage of households that did not have any housing expenditure due to special considerations81, represented at 11.3% of total sample size.
81 Special considerations could imply that the occupant was in transit, or rent/expenditure is provided by a third party, i.e. developer company or charitable organization.
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Exchange Rate as of Ringgit Malaysia US Dollar Australia Dollar 10th April 2013 (RM) (USD) (AUD) (XE, 2013). Average Income 1000 to 2500 330 to 825 314 to 784
Rent 300 to 750 99 to 248 94 to 235
Electricity 100 to 250 33 to 83 31 to 78
Other Utilities 60 to 150 20 to 50 18 to 47
Table 7.14 Exchange Rates for Income, Rent, Electricity and Other Utilities to USD and AUD Currency Source: (XE, 2013)
There were a small percentage of households (0.75%) that spends more than RM 300 a month on rent or housing loan repayments. The remaining households either spend less than RM 50 monthly for rent or housing loan repayment (0.4%), or between RM 150 to RM 300 a month (1.05%). Therefore about 88% of households spend less than RM 300 monthly on rent or housing loan repayment, which is lower than the 30% affordability range of monthly household income (refer Table 7.15). This is also presented in a pie chart to further illustrate the findings (refer Figure 7.11).
Between Between Between Between Between Rent/Housing Below RM50 RM100 RM150 RM200 RM250 Above Total Loan N/A82 Repayment RM50 to to to to to RM300 (%) RM 100 RM150 RM200 RM250 RM300 PPR Beringin 0.8 0.0 78.3 0.8 0.0 0.0 0.8 19.4 100 (%) PPR Intan 0.0 0.0 94.7 0.7 0.7 0.0 0.7 3.3 100 Baiduri (%)
Average (%) 0.4 0.0 86.5 0.7 0.35 0.0 0.75 11.3 100 Combined 0.4 0.0 86.5 1.05 0.75 11.3 100 Average (%) Combined 87.95 0.75 11.3 100 Total (%) Table 7.15 Average Percentage of Monthly Expenditure for Rent/Housing Loan Summary (n=281)
82 N/A in this context means that information of household income and expenditure were not disclosed by the respondents, or that household income and/or expenditure were paid by a third party (i.e. their own children, the developer company, or charitable organization) where the respondents did not have access to such information. This is applied to all survey findings for this household income and affordability section of the questionnaire.
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Average Percentage (%) of Monthly Income Spent for Rent/Housing Loan Repayment 1% 1% N/A 11% Below RM50 Between RM50 to RM 100 Between RM100 to RM150 Between RM150 to RM200 87% Between RM200 to RM250 Between RM250 to RM300 Above RM300
Figure 7.11 Average Monthly Expenditure for Rent/Housing Loan by Percentage (%) of Households
7.4.2 Monthly Expenditure for Electricity
Based on the survey findings, the majority of households (85%) spend no more than RM 100 per month on electricity. About 44 % of households spend less than RM 50 a month, and 41% spend between RM 50 to RM 100 a month on electricity. Therefore, a combined average of approximately 98% of households surveyed spend no more than RM 200 a month on electricity (refer Table 7.16). In comparison with the average household income range of RM 1000 to RM 2500 a month, 10% affordability should lie between RM 100 and RM 250 a month. This suggests that electricity is affordable for the sample size surveyed.
Between Between Between Between Between Electricity Below RM50 RM100 RM150 RM200 RM250 Above Total N/A Expenditure RM50 to to to to to RM300 (%) RM 100 RM150 RM200 RM250 RM300 PPR Beringin 47.3 39.5 6.2 3.9 0.8 0.8 0.0 1.6 100 (%) PPR Intan 41.5 42.1 7.2 6.6 1.3 0.7 0.7 0.0 100 Baiduri (%)
Average (%) 44.4 40.8 6.7 5.2 1.1 0.7 0.3 0.8 100
Combine 85.2 13 1.0 0.8 100 Average (%) Combined 98.2 1.0 0.8 100 Total (%)
Table 7.16 Average Percentage of Monthly Electricity Bill Summary (n=281)
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However, there is a small percentage of 0.8% of households where electricity expenditure information was not available, or was not applicable to the household under special considerations83. The remaining 1% of households spend more than RM 250 a month on electricity while 0.7% of households spend between RM 250 to RM 300, and 0.3% spend above RM 300 a month. Thus a small number of households exceed the 10% international affordability standard of RM 100 to RM 250 for monthly electricity. This is also presented in a bar chart to further illustrate the findings (refer Figure 7.12).
Average Percentage (%) of Monthly Income Spent for Electricity
1% 1% 1% N/A 7% 5% Below RM50 Between RM50 to RM 100 44% Between RM100 to RM150 Between RM150 to RM200 41% Between RM200 to RM250 Between RM250 to RM300 Above RM300
Figure 7.12 Average Monthly Expenditure for Electricity by Percentage (%) of Households
7.4.3 Monthly Expenditure for Other Utilities
Other utilities in this context refer to facilities such as water, telephone, internet and/or satellite (cable) television bills. The findings found that 79% of households spend less than RM150 a month on other utilities. The remaining 21% of households spend either between RM 150 to RM 300 a month (20%) or more than RM 300 a month (1.1%) for other utilities (refer Table 7.17).
The international average of other utilities expenditure is 6% of household income (Australian Bureau of Statistics, 2011; Central Bank of Malaysia, 2010; Department of Statistics, 2011a; Department of Statistics Singapore, 2008; Fankhauser & Tepic, 2007; ILO, 2010; Ministry for the Environment, 2009). The findings suggests that majority of
83 Special consideration in this context means that expenditure were paid by a third party (i.e. their own children, the developer company, or charitable organization) where the respondents did not have access to such information.
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Between Between Between Between Between Other Utilities Below RM50 RM100 RM150 RM200 RM250 Above Total N/A Expenditure RM50 to to to to to RM300 (%) RM 100 RM150 RM200 RM250 RM300 PPR Beringin 35.7 23.3 17.1 14.0 7.0 2.3 0.8 0 100 (%) PPR Intan 36.8 20.4 24.3 12.5 4.6 0 1.3 0 100 Baiduri (%)
Average (%) 36.3 21.8 20.7 13.2 5.8 1.2 1.1 0 100
Combined 36.3 42.5 20.2 1.1 0 100 Average (%) Combine Total 78.8 20.2 1.1 0 100 (%)
Table 7.17 Average Percentage of Monthly Expenditure for Other Utilities Summary (n=281)
Average Percentage (%) of Monthly Income Spent for Other Utilities
1% 1% N/A 6% 13% 36% Below RM50 Between RM50 to RM 100 Between RM100 to RM150 21% Between RM150 to RM200 Between RM200 to RM250 22% Between RM250 to RM300 Above RM300
Figure 7.13 Average Monthly Expenditure on Other Utilities by Percentage (%) of Households
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7.4.4 Summary of Operational Affordability for PPR Low-Cost Housing Projects
In summary, the average household income was established at RM 1000 to RM 2500 a month for the majority (61%) of households surveyed. Thirty per cent of this range is between RM 300 and RM 750 a month for rent or housing loan repayment, which is defined as affordable. The range of monthly rent or housing loan repayment was lower than the 30% affordability standard, where the majority of households (88%) spends between RM 100 to RM 150 a month. The RM 100 to RM 150 range is approximately 6% to 10% of average household monthly income. Another 0.75% of households spent more than RM 300 a month for rent or housing loan repayment. Therefore it can be concluded that for majority of households (88%), the PPR housing units are affordable in terms of their rent or housing repayment expenditure.
The standard of affordability for electricity expenditure was set at 10%, according to the international benchmark (Bujang, 2006; Fankhauser & Tepic, 2007; Zebardast, 2006). It represents RM 100 to RM 250 of average monthly household income. It was found that 98% of total households spent less than RM 250 a month for electricity. The average household expenditure for electricity can be assumed to be approximately RM 50 a month, based on the information provided by respondents of the household surveyed, not actual electricity bills. This assumption is reflected by the two highest percentages of household expenditure on electricity, i.e. less than RM 50 a month (44%), and between RM 50 to RM 100 a month (41%). This suggests that electricity is affordable for the majority of households in the PPR housing projects and the *Building Stock, as the low tariff rates are maintained by high government subsidizes (refer Appendix 1.1 for the tariff rates).
In terms of other utilities including water, telephone, internet and satellite (cable) television bills, approximately 79% of households spend less than 6% of average household income on these services. The other 20% spends between RM 150 to RM 300 a month, and remaining 1% spends more than RM 300 a month. The average household expenditure for other utilities can be assumed to be approximately RM 50, also reflected by the two highest percentages of household expenditure on other utilities, i.e. less than RM 50 a month (36%), and between RM 50 to RM 100 a month (22%).
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Therefore, in calculating the average combined expenditure for both electricity and other utilities, the total would be approximately RM 100 a month. This consequently shows that the combined operational costs for electricity and other utilities is approximately 10% of average household monthly income (refer Table 7.18). These analyses are also presented on a bar chart to better illustrate the conclusions (refer Figure 7.14).
Apportionment of Operational Cost (%) to Average Monthly Household Income of RM 1,000 to RM 2,500
International Survey Findings Average Operational Benchmark Expenditure
Rent 30 % < RM 300 (89%); RM 100 to RM 150 (RM 300 to RM 750) > RM 300 (0.8%); (89%) N/A (11%).
Electricity 10% < RM 250 (98%); < RM 50 to RM 100 (85%) (RM 100 to RM 250) > RM 250 (1%); N/A (0.8%).
Other Utilities 6% < RM 150 (79%); < RM 50 to RM 100 (58%) (Water, telephone (RM 60 to RM 150) RM 150 to RM 300 (20%); bills, internet, > RM 300 (1%); satellite television) N/A (0%)
Table 7.18 Affordability Analysis
Figure 7.14 illustrates the combined survey findings data, in terms of percentage (%) of households surveyed (Axis Y), for average monthly income and proportioned operational expenditure for rent/housing loan repayment, electricity and other utilities (Axis X). Figure 7.14 further indicates the majority (61%) household’s average monthly income of RM 1,000 to RM 2,500 (marked in dotted line and labelled ‘Average Household Income Range’). The bar chart is further manipulated to indicate where 10% of the average monthly household income range would be located in the proportioned operational expenditure of rent/housing loan repayment, electricity and other utilities (marked in dotted line and labelled ‘10% of Average Household Income Range’). This consequently illustrates that the majority of household’s monthly operational expenditure lies within, or less than the 10% of the average household income range. The data for percentage (%) of household’s operational expenditure lying within 10% of the average household income range is also presented in Table 7.19.
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10% of Average Average Household Income Household Income Range Range 100
90
80
70 Average Monthly Other 60 Utility
Average Monthly Electricity
of Households Households of 50
40 Average Monthly Rent Surveyed
30 Average Monthly Income 20 Percentage (%) Percentage
10
0
Monthly Household Expenditure Monthly Household Income
Figure 7.14 Average Percentage of Monthly Income and Expenditure Analysis (n=281)
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Between Between Between Between Between Below RM50 RM100 RM150 RM200 RM250 Above Total Average (%) N/A RM50 to to to to to RM300 (%) RM 100 RM150 RM200 RM250 RM300 Rent/Housing Loan 0.4 0.0 86.5 0.7 0.35 0.0 0.75 11.3 100 Repayment Combined 87.95 0.75 11.3 100 Average (%)
Electricity 44.4 40.8 6.7 5.2 1.1 0.7 0.3 0.8 100 Expenditure
Combined 98.2 1.0 0.8 100 Average (%)
Other Utilities 36.2 21.8 20.7 13.2 5.8 1.2 1.1 0 100 Expenditure
Combined 97.9 2.3 0 100 Average (%)
Table 7.19 Percentage of Household’s Operational Expenditure within 10% of Average Household Income Range (n=281)
Based on this analysis the percentage associated with energy and other utility costs is within the range of the international average of 10% and 6% of household income, respectively (Australian Bureau of Statistics, 2011; Central Bank of Malaysia, 2010; Department of Statistics, 2011a; Department of Statistics Singapore, 2008; Fankhauser & Tepic, 2007; ILO, 2010; Ministry for the Environment, 2009). However, to investigate the true meaning of affordability of housing for the public PPR housing projects, other expenditure such as transportation expenses, school fees and related expenses need to be investigated. This research is limited to only presenting operational costs of rent/housing loan repayment, electricity and other utility bills such as water, telephone, internet and/or satellite (cable) television. This presents an opportunity for continued research in this area.
7.5 Summary and Implications
The second aim of this research was to investigate the end-use pattern of electricity consumption for households in PPR low-cost housing projects. A face-to-face survey was carried out, using a random sampling approach. A total of 281 household units was selected from 27,102 *Building Stock, with 129 household units surveyed in PPR Beringin and 152 household units in PPR Intan Baiduri. The sample size of 281 household units surpasses the minimum units (266 units) needed to justify the population or the *Building Stock. It was found that 85% of the households were
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Most importantly, policy tools such as energy efficiency labelling policy for refrigerators and artificial cooling appliances could significantly help reduce energy consumption and related GHG emissions for average households. The findings showed that approximately 47% of all households leave their fans switched on for 24 hours, while another 47% leave their fans switched on for more than 6 hours daily. This implies that the daily operating time for cooling purposes is between 6 to 24 hours daily for the majority of households (94%). This heavy reliability on artificial cooling appliances indicates that natural ventilation is insufficient to maintain a comfortable indoor temperature.
Water heating was not available for most households in both PPR, where approximately 76% of household surveyed had not installed with hot water systems. This indicates that hot water systems do not play a significant part of the average household electrical appliances ensemble. On the other hand refrigerators play a significant role in a household, as 97% of households surveyed had refrigerators within their individual units.
Findings from the survey also indicate that an 43.8% of households earn between RM 1,000 to RM 2,000 a month. This is broken down to households with monthly income range between RM 1,500 to RM 2,000 a month (24%), while households earning between RM 1,000 to RM 1,500 a month is accounts for 20%. Most households in PPR Beringin and PPR Intan Baiduri spend approximately between RM 100 to RM 150 monthly for rent or housing loan repayment, at 78% and 95% respectively. This provides a combined average of 86.5%. A majority of households spend below RM 50 per month for electricity with a combined average of 44%, while 36% of households spend less than RM 50 per month for other utilities such as water, telephone, internet and/or satellite (cable) television.
In terms of affordability of electricity, it was found that on average, households spend approximately RM 50 per month, while the average household income was between RM 1000 to RM 2500 a month. Thus approximately 5% of household income is spent on electricity. Therefore it can be concluded that electricity is affordable for the average household in PPR low-cost housing projects. In calculating the affordability of
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Chapter 8: Conclusions and Recommendations
8.1 Reprise
This study has addressed the knowledge gap in energy performance and GHG emissions from building operations in the Malaysian public low-cost housing building typology. In successfully adopting the UNEP-SBCI’s Common Carbon Metric into the Malaysian context, this research has provided new knowledge on base-line electricity consumption to informing better policies for energy efficient low-cost housing in Malaysia. The MRV data collected for the often ‘invisible’ public low-cost housing typology should lead to a better informed baseline development for the Malaysian building sector. With better environmental and energy related policies in the building sector, Malaysia reduces its ‘carbon lock-in’ risk, and gains prospect for Clean Development Mechanism (CDM) and Nationally Appropriate Mitigation Action (NAMA) projects under the UNFCCC Kyoto Protocol, Article 12.
This Chapter reviews key elements of the research and discusses the implications of the findings in relation to the research aims. The main intent of this research has been to collect and present measurable, reportable and verifiable (MRV) data for policy development. Consequently, Chapter 2 defined the project-specific baseline as the most appropriate baseline that produces case-specific energy performance and GHG emissions data to inform policy. In supporting the rationale, Chapter 3 highlighted the absence of a mandatory energy efficiency or energy performance building code for the residential sector. Chapter 3 also emphasized the lack of environmental research on the low-cost housing typology and the opportunity to investigate end-use patterns of electricity consumption and operational costs of rent, electricity and other utilities to further inform policy makers.
Chapter 4 justified the two methods of investigation most appropriate to the research objectives and context, i.e. the UNEP-SBCI’s Common Carbon Metric (CCM) to calculate indirect GHG emissions, and a survey using a questionnaire to measure end-use patterns of electricity consumption and investigate operational affordability for households of the PPR low-cost housing projects. From the research methodology (Chapter 5), Chapters 6 and 7 discuss the case study findings and analyses according to the research aim. The case study collected electricity bills for 383 household units and
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Therefore, the case study findings presented three sets of MRV data: 1) operational energy performance and GHG emissions from household electricity consumption, 2) end- use patterns of electricity consumption of households, and 3) percentage of household income spent on rent, electricity and water utilities. The electricity bills presented energy performance and GHG emissions from building operations, using UNEP-SBCI’s CCM top-down and bottom-up approaches. The survey presented the average percentage of monthly income spent on electricity, rent and other utilities. The findings are analysed as separate policy recommendations corresponding to the three data sets. The research questions presented in Chapter 1 are addressed here accordingly:
1) How GHG baseline emission data can influence policy development related to energy efficiency in the Malaysian building sector?
a. What are the different types of baselines and how does it influence policy? i) There are three types of baselines; bottom-up baselines that consists of project-specific and multi-project baselines, top-down baselines, and hybrid baselines. Baseline models are used to calculate energy performance and GHG emissions, and forecast future projections in informing and monitoring mitigation policy. Baselines are also used to highlight key focus areas in developing such mitigation or conservation policies.
ii) The UNEP-SBCI’s Common Carbon Metric was identified as the most appropriate tool to present MRV data on energy performance and GHG emissions from building operations in countries where baselines do not exist. Other GHG accounting tools are not consistently comparable and rely heavily on simulated, not measured building performance.
b. What are the key energy efficiency and emissions related issues affecting the Malaysian building sector? Malaysian cities are experiencing urbanization that consequently risk carbon lock-in given the absence of energy efficiency policies. Conversely, even though
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Malaysia announced a 40% GHG emissions reduction target (by 2020 from its 1990 levels), the building sector has yet to produce an operational framework to reduce its environmental impact and/or play a significant role in achieving this aim. There is also lack of baseline research in the building sector and MRV data for different typologies are limited, which consequently limits GHG monitoring and/or mitigation work. Other issues such as carbon lock-in, energy poverty and the rebound effect are not yet the focus of research and development in the Malaysian building sector.
c. What energy-related policies are currently implemented in the Malaysian building sector? There are currently no mandatory energy efficiency or energy performance building codes currently implemented in the Malaysian building sector. There is however a voluntary guideline for energy efficiency for non-residential buildings (MS 1525:2007 – Malaysian Code of Practice on Energy Efficiency and the Use of Renewable Energy for Non-residential Buildings), and the Green Building Index (GBI) rating scheme.
d. What indicators can be used to inform policy makers in developing energy efficiency policies for the Malaysian building sector? The need for MRV data relays a quantification of electricity use and associated GHG emissions, which have been argued as an appropriate indicator for the current and projected impact on the climate and its people (particularly low- income households), in the absence of energy efficiency policies. Collating MRV data to produce baselines would allow policy makers to monitor the building sector’s progression towards GHG reduction.
2) What is the operational energy performance and related GHG emissions level of public low-cost housing in Malaysia?
a. How should low-cost housing be assessed for its energy performance? Use energy performance metrics that are measureable, reportable and verifiable,
in terms of kWh/m2/year or kWh/occupant/year, and kgCO2e./m2/year or
kgCO2e./occupant/year.
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b. What is the operational energy-related GHG emissions baseline of this building type? i) Top-down Approach (*Building Stock - 27,102 units of PPR low-cost housing in Kuala Lumpur)
= 60 kgCO2e./m2/year, or 761 kgCO2e./occupant/year
ii) Bottom-up Approach (Sample size – 383 units of PPR Intan Baiduri and PPR Beringin)
= 32 kgCO2e./m2/year, or 406 kgCO2e./occupant/year
3) What is the end-use electricity consumption patterns and operational affordability in low-cost housing?
a. What is the most appropriate method of investigation and what are the indicators? i) A household survey employing a questionnaire is the most common method of investigating a range of phenomena including measuring end- use electricity consumption patterns and household expenditure.
ii) Indicator for end-use patterns: hourly operating time of electrical appliances by type/function in its daily average.
iii) Indicator for operational affordability: percentage of household income spent for rent, electricity and other utilities.
b. What is the average hourly operating time of electrical appliances in the average household? i) Artificial cooling appliances = between 6 hours and 24 hours per day (47%: 47% = 94% of households) ii) Refrigerators = 24 hours daily (97% of households) iii) Entertainment and technology appliances = More than 6 hours daily (54% of households)
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c. What are the significant contributors to end-use electricity consumption in the average household? i) Artificial/mechanical cooling appliances Approximately 99.6% of households have installed appliances for cooling needs such as ceiling fans, standing fans, and/or air-conditioners. The operating time for cooling appliances mainly refers to fans, as only 6.4% of households had air-conditioning systems installed. Additionally, artificial cooling appliances have been found to be the second highest energy consuming appliance based on previous research, at an average of 2 kW.
ii) Hot water systems Although it was found that hot water systems were not a significant installation within the average PPR household (only 24% had hot water systems), the average energy consumption of such installation is the highest among other types of electrical appliance, at 2.35 kW.
iii) Refrigerators Approximately 98% of households have installed refrigerators in their apartment units. However refrigerators were not found to be a high energy consuming appliance, at an average of 0.25 kW.
d. What is operational affordability for low-income households in the PPR low-cost housing? i) Percentage of household income spent for rent per month: Between 6% to 10% of average household income (88% of sample size) ii) Percentage of household income spent on electricity per month: Approximately 5% of average household income (85% of sample size) iii) Percentage of household income spent on other utilities per month? Less than 6% of average household income (79% of sample size)
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Answering the research questions with the case study findings therefore reached the specific objectives of the research project which were:
1) To measure energy performance and operational GHG emission using the international Common Carbon Metric in the Malaysian context; 2) To investigate the end-use electricity consumption pattern of households; and 3) To investigate percentage of household income spent on rent, electricity and other utilities, to examine operational affordability.
The following sections discuss the research findings and the contribution to knowledge that has been made through this research. The value of undertaking further research in the future is also discussed interchangeably.
8.2 End-Use Electricity Performance and Operational GHG Emissions and Informing Policy
The first research question was aimed at understanding policy development regarding energy efficiency in the building sector. It was also a catalyst in informing the research methodology and case study protocol. As argued in Chapters 1 and 2, measurable, reportable and verifiable (MRV) data are a significant part of policy development. MRV data on GHG emissions from building operations, enables the development of a baseline relating to the performance of current building stock, which also enables the prediction of future emission levels (OECD & IEA, 2009). Baselines are vital for measuring emissions reduction and their benefits, and there is lack of such baselines in many countries due to the absence or lack of enforcement of energy efficiency standards (UNEP, 2009).
8.2.1 Developing GHG Performance Baseline for Malaysian Building Stock
UNEP-SBCI’s CCM in the Malaysian context calculated energy and GHG emissions for the building operations of PPR low-cost housing in Kuala Lumpur (*Building Stock) using the top-down approach. The bottom-up approach calculated energy and GHG emissions for 383 household units in two PPR low-cost housing projects in the Kepong district in Kuala Lumpur. This answers the second research question. Further research using UNEP-SBCI’s CCM in converting end-use electricity bills for different typologies could also be the starting point to generate a national-scale energy and GHG emissions baseline.
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Energy consumption for the *Building Stock, with a population size of 27,102 household units, provided an estimate of 133 kWh/m2/yr or 1674 kWh/occupant/yr while the sample of 383 household units provided a calculated total of 75 kWh/m2/yr and 940 kWh/occupant/yr. The *Building Stock’s GHG emissions total was 60 kgCO2e./m2/yr or
761 kgCO2e./occupant/yr while the sample provided a total of 32 kgCO2e./m2/yr or 406 kgCO2e./occupant/yr. The performance metrics are presented here again in Table 8.1.
Energy Consumption GHG Emission Performance
Metrics kWh/ kWh/ kWh/ kgCO2e/ kgCO2e/ kgCO2e/ m2/yr occupant/yr household/yr m2/yr occupant/yr household/yr
Top-Down 133 1674 8,370 60 761 3,805 Approach
Bottom-Up 75 940 4,700 32 406 2,030 Approach
7,789 (Medium Ariffin (2012) - - - - - Density)
3,012 Noordin - - (Malaysian - - - (2012)84 Average) TNB (1999) 2,754 (Malaysia cited in - - - - - Average) Tang (2005)
2,200 (Malaysia Tang (2005) - - - - - Average)
Table 8.1 Research Findings in Comparison to Other Research
The table also presents indicates that electricity use is 8,379 kWh/household/yr for the top-down and 4,700 kWh/household/yr for the bottom-up approaches, with an average of five persons per household. Energy consumption for both top-down and bottom-up approaches were higher than the World Energy Council’s world average of 3,500 kWh/household/yr (WEC, 2010). However, in comparison with Noordin (2012) estimation, the average Malaysian household (comprising of 4.31 persons per household) consumes approximately 3,012 kWh/household/yr (Department of Statistic Malaysia, 2011; Noordin, 2012).
84 It must be noted that the average data for household GHG emissions used in this research is based on a household unit consisting of 4.31 persons, according to the 2010 Population and Housing Census (Department of Statistic Malaysia, 2011; Noordin, 2012).
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Therefore, for a consistent comparison, the top-down and bottom-up findings of 1,674 kWh/occupant/yr and 940 kWh/occupant/yr are multiplied by 4.31 persons per household. This consequently provided an estimate of 7,215 kWh/household/yr for the top-down approach, and 4,051 kWh/household/yr for the bottom-up approach. Both top- down and bottom-up findings from the case study are higher than the estimated average of Malaysian household energy consumption for 2011.
However in comparison to Ariffin (2012) calculation, the Bottom-Up analysis were lower than the calculated average of a typical Malaysian medium density house that consumes approximately 7,789 kWh/household/year (Ariffin, 2012), which could suggest that lower density housing consumes more energy. Although the opposite is suggested as the Top- Down analysis calculated the PPR household energy consumption was higher than the medium density housing. Further research should be done through all range of density and typology in the Malaysian residential sector, in order to provide a more accurate representation of energy consumption in order to inform the development of energy efficiency policy.
This data suggest that households in public PPR low-cost housing projects are consuming high levels of electricity. This is a concern as electricity consumption is expected to grow, and has already surpasses initial predictions of previous studies. The high level of electricity consumption is due to the relatively cheap costs of energy services, provided by government subsidies at an average of 75% discount on gas prices in the production of electricity (Energy Commission, 2011a), which is also likely to cause a direct rebound effect. A national scale investigation of household electricity consumption is needed to further develop this argument, a future research area within this field.
These findings address the research question of how the operational energy performance of low-cost housing in Malaysia can be assessed and provide an indicative base-line of electricity consumption in a particular low-cost housing typology. The case study therefore provides a starting point to generate a national energy and GHG performance baseline for the residential building typologies, based on measured performance. The results indicate that electricity consumption in low-cost housing in Malaysia is being significantly underestimated which presents a lock-in risk. The findings support the need to develop energy-efficiency policies for low-cost housing in Malaysia.
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8.2.2 End-Use Patterns of Electricity Consumption in PPR Low-Cost Housing
As Malaysia moves towards developed country status by the year 2020, its end-use electricity consumption is projected to increase significantly as households attempt to increase their indoor environmental comfort (Tang, 2005). Understanding how end-use energy is consumed is instrumental in predicting future consumption patterns and/or problems in different building typologies and profiles (Tsuji et al., 2004). The findings of this research support the conclusions of other research done by the New Energy and Industrial Technology Development Organization (NEDO) (2004), Centre for Environment, Technology and Development Malaysia (CETDEM) (2004), Tang (2005), Saidur (2007), and Noordin (2012) in monitoring end-use energy consumption of households and in developing a nationwide energy baseline for residential buildings.
The research successfully reported on end-use pattern of electricity consumption for 281 households that were surveyed in two public low-cost housing projects in Kuala Lumpur, i.e. PPR Beringin and PPR Intan Baiduri. The end-use electricity findings presented certain patterns of electrical appliance consumption. For example, hot water systems were not a significant electrical appliance in the average household, as only 24% of households had installed hot water systems and utilized it for approximately 1 hour/day. In contrast, other appliances for lighting, cooling, refrigeration, and entertainment and technology purposes played a significant part in the average household. All of the households surveyed (100%) had lighting appliances and approximately between 98% to 99% of households had cooling, refrigerators, and entertainment and technology appliances installed in their apartment units. Meanwhile, approximately 78% of households had at least one type of cooking and kitchenware electrical appliance or equipment in their apartment units. Finally, approximately 85% of households had washing machines and used them on a daily basis for approximately 1 hour/day.
Additionally, the case study highlighted the internal thermal condition of these apartment units, as almost half of the households surveyed (46.9%) utilized cooling appliances on a 24-hour basis. The other half of the households surveyed (46.6%) utilizes their cooling appliances for more than 6 hours a day, on average. These findings could indicate that the internal thermal conditions of these apartment units without cooling appliances are uncomfortable.
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In comparison with other research done, the average operating time for cooling purposes was 11.3 hours/day (Tang, 2005) or 16.7 hours/day (Saidur et al., 2007). However, the research findings highlighted that almost half of the households surveyed (47%) had 24 hour/day operating time for cooling purposes, which is higher than the average operating time calculated in previous research done in Malaysian households. This significant increase in operating time warrants attention so that strategies may be developed to reduce cooling loads. Energy efficient labelling policy for cooling appliances and refrigerators, as the most predominantly used electrical appliance type daily, could decrease energy consumption.
8.2.3 The Need for Developing Energy Efficiency Building Codes
In Malaysia low-cost housing construction standards are controlled by the Uniform Building By-Laws 1984 and the Construction Industry Standards, both of which currently impose no energy efficiency requirements. This is a missed opportunity for saving energy and improving thermal comfort in low-cost housing. The research findings clearly indicate that majority of households’ (94%) operating time for artificial cooling is between 6 to 24 hours daily, which indicates that natural ventilation is insufficient to maintain a comfortable indoor temperature. Buildings in Malaysia should be constructed according its tropical climate classification to reduce cooling loads, as Malaysia experiences a constant hot and humid temperature all year round (except in the highlands such as Cameron Highlands, Genting Highlands and so forth).
Therefore, there is a need to development energy efficient building codes and labelling policy to help reduce overall energy consumption and related GHG emissions in Malaysia. Case study findings of end-use patterns of electricity consumption for the PPR low-cost housing households can be used to further estimate a city scale average, as the sample size surveyed is statistically representative of the existing PPR low-cost housing units in Kuala Lumpur, i.e. the *Building Stock. The survey was also successful in determining the most significant electrical appliance for end-use electricity consumption patterns; hence recommendations can be made according to key issues concluded in the analysis.
However it is recognized that investigating the effectiveness of building energy codes is vitally important in order to help the building sector reduce its energy consumption. Analysing operational energy data or measured building energy performance and
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Examples of energy efficiency and conservation strategies for the tropical climate were presented in Chapters 1 and 2. Energy efficiency and conservation policies should not only consider new building but also existing buildings, in efforts to reduce energy consumption and the related GHG emissions of the current building stock. For example, retrofit strategies to reduce energy consumption can be implemented through introducing insulation for cavity walls and roof fittings (UNEP, 2007).
End-use electricity consumption patterns indicate that the design specifications of the household units need to be revised in terms of bio-climatic design and site specific planning. Incorporating these two features into the Construction Industry Standard (CIS) will help reduce electricity consumption for cooling purposes, with regards to site- specific planning characteristics to reduce heat transfer and improve natural ventilation. This is vitally important as Malaysia is located in the tropical region and the provision of energy efficient indoor cooling should be listed as a top priority.
The CIS 2 (for low-cost housing) site planning and unit design specifications also do not include strategies useful to Malaysia’s climatic conditions. In contrast, Singapore’s Building Control (Environmental Sustainability) Regulations (BCESR) specifies a minimum standard for residential buildings thermal performance to minimize heat gain and consequently reduce cooling loads (25 W/m2) of permissible residential envelope transmittance value (RETV) (BCA, 2012b). Singapore’s BCESR also encourages designers to utilizes prevailing winds, provide cross ventilation and orientate window openings to the north and south directions (BCA, 2012b). The BCESR also requires all new buildings (gross floor area of 2,000 m2 or more), residential and non-residential alike, to comply with a minimum Green Mark score of 50 points (BCA, 2012b). A similar policy could be implemented in Malaysia, using the Malaysian Green Building Index (GBI) as a mandatory requirement.
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Although Singapore’s environmental sustainability regulation is not specifically looking at low-cost housing construction, similar statements can be made in the Malaysian CIS as Malaysia is located in the same climate zone as Singapore. Moreover, introducing such energy efficiency and conservation strategies into the CIS would benefit PPR low- cost housing projects as they are standardized nationally, and must be in compliance with CIS 1 and 2. Enhancing and implementing the current voluntary Malaysian Standard Code of Practice on Energy Efficiency and the Use of Renewable Energy for Non-residential Buildings (MS 1525:2007) for the residential typology could also be a starting point in energy efficient policy development.
However, low-cost housing in Malaysia is typically constructed with design assumption to maximize passive operations for lighting and ventilation, due to the low economical matrix and low price range. The current MS 1525:2007 are catered for fully air- conditioned non-residential buildings with high reliance on HVAC systems, and not for passive or natural ventilation. The research findings suggest that only a small percentage of households surveyed (6.4%) has installed air-conditioning systems, therefore the Construction Industry Standards 1 and 2 should focus on how to improve natural lighting and ventilation in reducing household energy consumption.
Additionally, the operating time differs between residential and commercial or other non-residential buildings, where residential buildings mostly operate after working hours and the opposite for commercial buildings that are highly operated during the day and within working hours. Therefore in adopting energy efficient policies, it is essential to differentiate energy saving and efficiency strategies for buildings using HVAC systems and does that do not, and strategize according to the building’s operating time.
8.4 Affordability of Electricity in PPR Low-Cost Housing (Case Study)
PPR public low-cost housing is aimed to help low-income households gain access to affordable housing in urban areas. However, affordability of these PPR units is defined only in terms of its rent or sale price. As indicated in Chapter 3, this research has adopted the definition of operational housing affordability in terms of percentage of monthly income spent on rent, electricity and water utilities, i.e. a maximum of 30% for rent or housing repayments, less than 10% for electricity, and less than 6% for other utilities of monthly household income. In conclusion, the findings show that the
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The case study findings indicated that operational costs of rent, electricity and other utilities such as water, telephone, Internet and/or cable television bills were considered affordable for households in the PPR low-cost housing projects. However, in calculating the combined average expenditure for both electricity and other utilities, the mode was calculated to be approximately RM 100 a month. This consequently shows that the combined operational costs for electricity and other utilities is approximately 10% of average household monthly income.
Additionally, other household expenses such as education, health, transportation and so forth were not investigated. Therefore this gap presents an opportunity for further research to include such household expenses in investigating true operational affordability for the low-income households in PPR low-cost housing projects. Such investigation should also be conducted nationwide in a long-term period, and at intervals to reflect current costs of living and living standards.
As Malaysian households enjoy highly subsidized electricity services, there is high risk of direct-rebound effect, which is presently not being investigated. Therefore, further research in the affordability of electricity can also be used as a mechanism to gauge the occurrence of any rebound effect in comparison to other developing countries with similar characteristics of electricity consumption and household income. Collaborative findings could inform policies, in terms of reallocating subsidies for designated electricity consumers (for example, households), or in other words restructuring distribution of energy subsidies (see Morgan et al., 2003). Strong linkages between energy use, health, social development and environmental impacts should be addressed in energy distribution policies, in order to assess long-term effects of subsidy policies (Morgan et al., 2003).
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8.5 Limitations to Research
Limitations were encountered throughout this research at different stages. This section discusses them accordingly.
Stage 1: Determining systems boundary & designing the case study protocol Needed to get authorization from many stakeholders before determining the systems boundary, mainly from the local authority managing the PPR low-cost housing projects and the National Energy Provider (Tenaga Nasional Berhad- TNB) company. Access to data is dependent on the authorization of these two main stakeholders.
Stage 2: Conducting the field work Needed to plan and reschedule time slots to get a suitable time to meet with appropriate personnel at TNB that would have access to household electricity data. In identifying appropriate personnel (Mr. Romadas, Meter Reader Examiner for KL-West Area, under the Distribution Division of TNB), there was a need to schedule time slots that was suitable for him, as accessing individual household electricity data was time consuming. This exercise was mostly conducted on the weekend. Additionally, access to data was solely dependent on Mr. Romadas’s cooperation and availability. Restrictions were also imposed to retain anonymity and confidentially of data retrieved from TNB. In conducting the survey, authorization and participation of each individual household unit was needed. This exercise was time consuming and often limited the number of households surveyed daily, averaging between 15 to 20 households per day. There was also issue of safety as this research was done individually. No further validation of the questionnaire findings was conducted, as respondents base the survey findings only on self-reported data. Questionnaire should consider providing a more specific time range or duration of operating time for the different electrical appliances to provide a more accurate representation of measured energy patterns.
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Stage 3: Analysing data The household electricity consumption collected from TNB could not be validated to individual household electricity consumption to retain anonymity of household data and difficulty in getting the collated households to participate in the survey. Verification of data can be recommended for future research projects. Analysis of individual household’s electricity consumption from utilized electricity bills could not detect which households had installed air-conditioning systems because the data was not collated to which households that was surveyed during the fieldwork period. The CCM’s top-down and bottom-up approach different analysis, which led to gap between the two analyses. This is largely due to dissimilarities found in the systems boundary for both approaches, and has been discussed in Chapter 6. However, deep investigation should be conducted to uncover the reasons for such discrepancy. Comparison with other national/international benchmark would be inaccurate due to the different methodologies and systems boundaries adopted, for both the CCM findings and survey questionnaire findings.
8.6 Implications and Contribution to Knowledge
This Chapter concludes the research by collating the case study findings with the research objectives. The GHG emissions reduction challenge for Malaysia is significant as the country rapidly becomes more urbanized. Undoubtedly, there is a pressing need to implement energy efficiency and conservation policies within the Malaysian residential sector, as higher levels of energy consumption are unavoidable. Moreover, Malaysia’s carbon lock-in risk is significant as the construction industry continues to build inefficient buildings and fails to implement appropriate energy efficiency policies.
The energy performance and GHG emissions calculated for the PPR low-cost households were significantly higher than the estimated and predicted levels derived from previous research, and of other international averages set by the World Energy Council and the International Energy Agency. Additionally, as electricity was found by household survey to be affordable with high government subsidies, there is a concern for a direct rebound effect that assumes higher levels of energy consumption due to the subsidized price of the energy services.
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The findings indicate that electricity consumption in public low-cost housing in Malaysia is being significantly underestimated which supports the argument of high inefficiency lock-in risk. In adopting the UNEP-SBCI’s Common Carbon Metric, the research has effectively produce findings from an accounting tool that inherited an internationally sound methodology in measuring energy performance and GHG emissions for the Malaysian context. The research project has successfully measured, reported and verified energy performance and operational GHG emission of two PPR low-cost housing projects in Kuala Lumpur. Therefore, recommendations made in this research are based on findings that are comparable and consistent with IPCC and UNFCCC’s baseline MRV data requirements.
It is also important to underline that this research is limited to the UNEP-SBCI’s Common Carbon Metric’s methodology in collating the top-down and bottom-up analyses. Nonetheless the significant contribution of this research lies in meeting its objectives and its emphasis on obtaining quantitative data. With that said, this research has: 1) Presented measurable, reportable and verifiable data and patterns of electricity consumption and their related GHG emissions of the public low-cost housing building typology; 2) Indicated operational cost and its affordability for public low-cost housing; 3) Suggested revisions of the Construction Industry Standard based on end-use patterns of electricity consumption; as 4) Discovering that electricity consumed in low-income households were higher than the estimated national average for the residential sector.
8.7 Further Research
In concluding the research, a few distinct directions for further research were established are recapitulated as:
1) Adopting the UNEP-SBCI’s Common Carbon Metric to measure operational energy and GHG emissions performance in different building typologies, to develop a comprehensive nation-wide baseline in monitoring the building sector’s environmental impact;
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2) Adopting a hybrid model to generate baseline data for energy consumption and GHG emission to reconcile the top-down and bottom-up analysis, and to investigate reasons for such discrepancy; 3) Research for end-use patterns of electricity can be further disaggregated in terms of specific operating time for each electrical appliances and validating the average end- use electricity consumed for each electrical appliances (in kWh) by using in-situ power meter reader for the collated household; 4) Case study findings of end-use patterns of electricity consumption for the PPR low- cost housing households can be used to further estimate a city scale average, as the sample size surveyed is statistically representative of the existing PPR low-cost housing units in Kuala Lumpur, i.e. the *Building Stock; 5) Investigate true long-term operational affordability in terms of other household expenses that lies beyond this research’s boundary, for example expenses for transportation, education, food supply, health and so forth, can be included in future research; and 6) A national scale investigation of household electricity consumption and compare operational affordability with other developing countries, with similar characteristics and climatic classification, in order to further investigate the impacts of relatively cheap energy services, i.e. the direct rebound effect.
The systems boundary defined for this research emphasized the environmental dimension of sustainability. Other social and economic dimensions of sustainability are contained in the building typology of public PPR low-cost housing projects and the demographics of low-income households surveyed. Therefore this research has contributed knowledge to the sustainability challenge in Malaysia and to the expanding argument that Malaysia must move towards a more sustainable built environment. Even though this research is limited in providing direct solutions, it has presented measurable, reportable and verifiable data for a building typology that is currently neglected environmentally and by policy makers.
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Appendix 1.1 Tenaga Nasional Berhad Domestic Tariff
Tariff rates for domestic85 or residential users provided by the National Energy Company (Tenaga Nasional Berhad – TNB) for 2012:
TARIFF CATEGORY UNIT RATES (RM)
Tariff A - Domestic Tariff For the first 200 kWh (1 - 200 kWh) per month cents/kWh 21.8 For the next 100 kWh (201 - 300 kWh) per month cents/kWh 33.4 For the next 100 kWh (301 - 400 kWh) per month cents/kWh 40.0 For the first 100 kWh (401 - 500 kWh) per month cents/kWh 40.2 For the next 100 kWh (501 - 600 kWh) per month cents/kWh 41.6 For the next 100 kWh (601 - 700 kWh) per month cents/kWh 42.6 For the next 100 kWh (701 - 800 kWh) per month cents/kWh 43.7 For the next 100 kWh (801 - 900 kWh) per month cents/kWh 45.3 For the next kWh (901 kWh onwards) per month cents/kWh 45.4 The minimum monthly charge is RM3.00
Electricity Tariff for Domestic User for 2012 Source: TNB (2012)
85 Domestic user in this context is defined as “a consumer occupying a private dwelling, which is not used as a hotel, boarding house or used for the purpose of carrying out any form of business, trade, professional activities or services” (TNB, 2012).
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Appendix 2.1 Carbon Disclosure Project Reporting Guidance Methodology (Carbon Disclosure Project, 2013b, 2013c)
Carbon Disclosure Project : CDP Cities Programme (2013) – Guidance for Carbon Disclosure Project – Guidance for Companies Reporting on Responding City Governments (p.15) Climate Change on Behalf of Investors and Supply Chain Members (2013) (p.86-88) Accepted Primary methodology: 1) IPCC models and climate change impact assessment guidance Accepted Methology: 2) OECD Strategic Environmental Assessment and Adaptation to Climate 1) ABI Energia Linee Guida Change 2) Act on the Rational Use of Energy 3) UNDP climate risk management methodologies 3) American Petroleum Institute Compendium of Greenhouse Gas 4) ICLEI climate adaptation methodology (ADAPT) Emissions Methodologies for the Oil and Natural Gas Industry, 2009 5) UK Climate Impacts Partnership Framework (UKCIP) 4) Australia - National Greenhouse and Energy Reporting Act 6) World Bank Urban Risk Assessment(URA) 5) Bilan Carbone 7) Economics of climate adaptation working group-Shaping climate 6) Brazil GHG Protocol Programme resilient development: A framework for decision making 7) Canadian Association of Petroleum Producers, Calculating 8) State or region vulnerability and risk assessment methodology Greenhouse Gas Emissions, 2003 9) Agency specific vulnerability and risk assessment methodology 8) China Corporate Energy Conservation and GHG Management 10) Other Programme 11) Unknown 9) Defra Voluntary Reporting Guidelines 12) No evaluation done 10) ENCORD: Construction CO2e Measurement Protocol 11) Energy Information Administration 1605B 12) Environment Canada, Sulphur hexafluoride (SF6) Emission Estimation and Reporting Protocol for Electric Utilities
13) Environment Canada, Aluminum Production, Guidance Manual for Estimating Greenhouse Gas Emissions 14) Environment Canada, Base Metals Smelting/Refining, Guidance Manual for Estimating Greenhouse Gas Emissions 15) Environment Canada, Cement Production, Guidance Manual for Estimating Greenhouse Gas Emissions
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16) Environment Canada, Primary Iron and Steel Production, Guidance 34) Regional Greenhouse Gas Initiative (RGGI) Model Rule Manual for Estimating Greenhouse Gas Emissions 35) Taiwan - GHG Reduction Act 17) Environment Canada, Lime Production, Guidance Manual for 36) Thailand Greenhouse Gas Management Organization: The National Estimating Greenhouse Gas Emissions Guideline Carbon Footprint for Organization 18) Primary Magnesium Production and Casting, Guidance Manual for 37) The Climate Registry: Electric Power Sector (EPS) Protocol Estimating Greenhouse Gas Emissions 38) The Climate Registry: General Reporting Protocol 19) Environment Canada, Metal Mining, Guidance Manual for Estimating 39) The Climate Registry: Local Government Operations (LGO) Protocol Greenhouse Gas Emissions 40) The Climate Registry: Oil & Gas Protocol 20) EPRA (European Public Real Estate Association) guidelines, 2011 41) The GHG Indicator: UNEP Guidelines for Calculating Greenhouse Gas 21) Hong Kong Environmental Protection Department, Guidelines to Emissions for Businesses and Non-Commercial Organisations Account for and Report on Greenhouse Gas Emissions and Removals 42) The Greenhouse Gas Protocol: A Corporate Accounting and Reporting for Buildings, 2010 Standard (Revised Edition) 22) ICLEI Local Government GHG Protocol 43) The Greenhouse Gas Protocol: Public Sector Standard 23) India GHG Inventory Programme 44) The Tokyo Cap-and Trade Program 24) International Wine Industry Greenhouse Gas Protocol and Accounting 45) US EPA Climate Leaders: Direct Emissions from Iron and Steel Tool Production 25) IPCC Guidelines for National Greenhouse Gas Inventories, 2006 46) US EPA Climate Leaders: Direct Emissions from Municipal Solid Waste 26) IPIECA's Petroleum Industry Guidelines for reporting GHG emissions, Landfilling 2003 47) US EPA Climate Leaders: Direct HFC and PFC Emissions from 27) IPIECA’s Petroleum Industry Guidelines for reporting GHG emissions, Manufacturing Refrigeration and Air Conditioning Equipment 2nd edition, 2011 48) US EPA Climate Leaders: Direct HFC and PFC Emissions from Use of 28) ISO 14064-1 Refrigeration and Air Conditioning Equipment 29) Japan Ministry of the Environment, Law Concerning the Promotion of 49) US EPA Climate Leaders: Indirect Emissions from Purchases/ Sales of the Measures to Cope with Global Warming, Superceded by Revision Electricity and Steam of the Act on Promotion of Global Warming Countermeasures (2005 50) US EPA Climate Leaders: Direct Emissions from Stationary Amendment) Combustion 30) Korea GHG and Energy Target Management System Operating 51) US EPA Climate Leaders: Direct Emissions from Mobile Combustion Guidelines Sources 31) New Zealand - Guidance for Voluntary, Corporate Greenhouse Gas 52) US EPA Mandatory Greenhouse Gas Reporting Rule Reporting 53) WBCSD: The Cement CO2 and Energy Protocol 32) Philippine Greenhouse Gas Accounting and Reporting Programme 54) World Steel Association CO2 emissions data collection guidelines (PhilGARP) 55) Other 33) Programa GEI Mexico
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Appendix 2.2 UNEP-SBCI’s Common Carbon Metric Building Definition (UNEP-SBCI, 2010b)
Building area: “is measured in meters squared (m2) of Gross Floor Area (GFA) or a building. GFA is measured from the exterior perimeter of the building envelope. Areas of internal walls & structure, shafts, elevators, and stairways are to be included in the GFA. The GFA is to be measured from the inside face of exterior perimeter wall, also including areas of sloping surfaces such as staircases, galleries, raked auditoria, and tiered terraces, but excluding open floors and exterior covered ways and balconies” (UNEP-SBCI, 2010b p.41)
Building type: “UNEP-SBCI has adopted the list of building types developed by the UNFCCC’s Clean Development Mechanism (CDM), at least for Phase II of the CCM” i.e. single-family residential, multi-family residential, other residential, office, hotel, warehouse & storage, mercantile & services, food service, entertainment, other commercial, education, public assembly, health care, public order &safety, institutional lodging, other institutional, mixed-use building units, other non-residential” (UNEP-SBCI, 2010b p.58-59).
Multi-family residential = includes apartments in a building (that comprises of more than two apartments); Single-family residential = includes construction for a single family or household, such as bungalows, cottages, stand-alone houses, semi-detached houses, town houses and row houses; and Other residential = includes residential building units that do not belong to any of the above categories (UNEP-SBCI, 2010b p.58).
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Appendix 3.1 Construction Industry Master Plan (CIMP)
CIMP Seven Strategic Thrusts Source: (CIDB, 2007a, p. 10)
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CIMP Key Performance Indicators Source: (CIDB, 2007a, p. 11)
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Appendix 3.2 Types of Public Low-Cost Housing in Malaysia
Public Low Cost Housing Integrated People’s Housing PPR NEW POLICY Programme – PAKR Programme for Rental - PPR (SALE) (RENT)
Purpose To provide proper housing To accommodate the In February 2002, Cabinet agreed to the proposed changes in for low income groups in squatters affected by policy and implementation strategies of the low cost housing rural and suburban areas as government development program in which the Public Low Cost Housing Programme well as basic amenities. projects around the Federal (PAKR), formerly State funded projects (through loans from the Territory of Kuala Lumpur Federal Government and implemented by NHD), will become and the Klang Valley in Federal funded projects in the name of Program Perumahan Selangor. Rakyat PPR Owned.
Cabinet also agreed to continue the implementation of the Integrated People’s Housing Programme for Rental to PPR Rent.
All homes built under the two programs PPR Owned and PPR Rented required to incorporate specifications for low-cost housing set forth in the National Housing Standards for Low Cost Housing One and Level Two (CIS 1) and the National Housing Standards for Low Cost Housing Flats (CIS2).
Continued
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PAKR PPR PPR Owned PPR Rented
Rationale To achieve the Government's As a means to achieve the goal To enable lower income groups For relocation of squatters, objective to improve quality of of zero squatters by 2005 have the opportunity to own especially in major cities as well as life and eradicate poverty their homes to generate economic growth through the housing sector and industry-related industries Approach State identifies and provides Implementing agency of the PPR National Housing Company NHD will continue to be the appropriate housing sites and is NHD. Construction costs are Limited (SPNB) has been given implementing agent for PPR Rented the tender offer. incurred by the Federal the responsibility of The housing construction program Government, but the land must implementing PPR Owned States receive financial loans under PPR Rent is intended to be be provided by the State. The from the Federal Government NHD with the State rented to squatters and will be site provided must be a site based on State Government and local extended to other low income ready for development. needs/requirements of the authorities coordinate the groups when the goals are achieved number of units to be built, NHD is fully responsible for the resettlement of squatters in the zero squatters. under the 5 year development PPR project including initial country. However the plan from Central agencies planning, the appointment of programme is only currently consultants, management and implemented in Pahang by its State Government provides a other tender. Implementation of State Government list of qualified buyers to buy this program is 'fast-track' with low-cost homes and funding some relaxation of the rules from Federal Government. given to accelerate the completion of the project.
(Continued) Types of Public Low-Cost Housing Source: (National Housing Department, 2001)
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Appendix 3.3 Final Electricity Consumption
Final Electricity Consumption in kilo tonnes of oil equivalent (ktoe) Year Agriculture Commercial Transport Industrial Residential Total 2000 0 1478 1 2805 975 5259 2001 0 1579 2 2930 1081 5592 2002 0 1698 2 3059 1161 5920 2003 0 1818 2 3242 1248 6310 2004 0 1979 2 3340 1319 6640 2005 0 2172 2 3371 1395 6940 2006 5 2272 2 3475 1515 7269 2007 16 2480 2 3587 1598 7683 2008 19 2598 15 3687 1668 7987 2009 21 2743 12 3719 1792 8287 2010 24 3020 18 3994 1937 8993 Total 85 23837 60 37209 15689 76880
% 0.11 31 0.08 48.4 20.41 100
Electricity Consumption According to Sector Source: Malaysian Energy Information Hub (Energy Commission, 2011b)
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Appendix 3.4 Average Operating Hour of Electrical Appliances
Distribution of Average Operating Time of Electrical Appliances According to Saidur et. al., 2007
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Average Daily Average Power Average Types of Electrical Daily Weekday Daily Weekend (Hour) (W) Power Appliance Average (Hour) Average (Hour) (kW) Artificial Lighting Fluorescent light 5.67 5.45 5.56 30 0.03 Bulb 3.45 3.38 3.42 70 0.07 Total Daily Average 8.98 Artificial Cooling Fan 11.55 12.04 11.8 60 0.06 Air-Conditioner 5.05 4.75 4.9 1385 1.39 Total Daily Average 34.66 Hot Water System Water heater 0.16 1.23 0.7 2000 2 Total Daily Average 10 Refrigeration Refrigerator-freezer 8.11 8.11 8.11 196 0.196 Total Daily Average 88.13 Entertainment and TV 5.70 8.15 6.93 80 0.08 Technology Hi-fi 2.45 1.93 2.19 20 0.02 Hand phone charger 3.51 3.31 3.41 30 0.03 Personal computer 1.97 1.82 1.9 100 0.1 VCD/VCR/DVD player 3.11 3.15 3.13 250 0.25 Total Daily Average 193.82 Cooking and Rice cooker 0.72 0.73 0.73 905 0.905 Kitchen Ware Blender/mixer 0.26 0.19 0.23 300 0.3 Toaster 0.16 0.14 0.15 800 0.8 Kettle 0.48 0.44 0.46 2125 2.125 Microwave oven 0.18 0.17 0.18 1125 1.125 Total Daily Average 389.39 Clothes Washing Washing machine 1.04 1.04 1.04 1005 1.005 Total Daily Average 779.82
Interpretation of Average Operating Time of Electrical Appliances According to Saidur et. al., 2007
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Distribution of Average Operating Time of Electrical Appliances According to Tang (2005)
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Appendix 4.1 MTEN PPR Housing List (City Hall of Kuala Lumpur, 2009c)
Program Perumahan Rakyat (PPR) – MTEN Year No. of No. of No. of # Name Zone Constructed/Completed Blocks Storey/Level Units 1 PPR Desa Petaling 1 2002 2 18 632 2 PPR Raya Permai 1 2001 4 18 1264 3 PPR Seri Malaysia 1 2007 2 18 632 4 PPR Kg. Muhibah 2 1999 9 17 2844 5 PPR Salak Selatan 2 1999 2 17 632
6 PPR Taman Beringin 3 1999 6 17 1896 7 PPR Wahyu 3 1999 3 17 948 8 PPR Pekan Baru 3 1999 2 17 632 9 PPR Intan Baiduri 3 2000/2003 6 17 1834 10 PPR Batu Muda 3 1999/2007 7 17 2132
11 PPR Pudu Ulu 1 2003 3 18 948 12 PPR Laksamana 1 2003 1 13 180 13 PPR Seri Alam 2 2000 5 10 660 14 PPR Seri Anggerik 2 2007 1 17 316 15 PPR Pantai Ria (LC ii) 2 2007 4 17 1264 16 PPR Cempaka (LC ii) 2 2007 2 17 632 17 PPR Kg Limau 2 1999 2 17 632 18 PPR Kerinchi 2 1999/2007 6 17 1896
19 PPR Seri Semarak 4 1999 5 17 1580 20 PPR Air Panas 4 1999 8 17 2528 21 PPR Sg Bonus 4 1999 2 17 632
22 PPR Perkasa 1 2004 3 15 880 23 PPR Hiliran Ampang 4 1999 3 17 948 24 PPR Laksamana 1 2003 3 17 560
TOTAL 91 399 27102
AVERAGE 3.79 16.625 1129.25
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Appendix 4.2 Sampling Size Calculation (QI Macros, 20120)
Confidence Level (Power) 90% Confidence Factors Confidence Interval 0.05 Percent Z Population (if known) 27,102 80% 1.28 85% 1.44 Attribute Data 90% 1.64 Percent defects (50%) 50% 95% 1.96 Sample Size (Unknown Population) 269 96% 2.06 Sample Size for Known Population 266 97% 2.17 98% 2.33 Variable Data 99% 2.58 Standard Deviation ([High-Low]/6) 0.167 Defaults Sample Size (Unknown Confidence Interval Population) 30 0.05 (precision +/-5% = 0.05) Sample Size for Percent defects (Attribute Known Population 30 50% - 50%) Standard Deviation 0.167 ([High-Low]/6)
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Appendix 4.3 Execution Plan for Field Work
Task 6.1 Planning , getting approval and scheduling
Task 6.2 Collect electricity bills from National Energy Company (TNB)
Task 6.3 Execute survey questionnaire
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Appendix 5.1a UNEP-SBCI Common Carbon Metric Top-Down Approach – Building Stock Characteristics STEPs 1-3
Characterise the Whole
What is the total area of the Whole, in m2? *Whole is the combined total of 24 PPR Housing of 27,102 units X 60m2 What tier of accuracy best describes your data on area? Calculated (m2) What is the total occupancy of the whole, in number of occupants? *Estimated 5 persons per unit, with What tier of accuracy best describes your data on occupancy? Calculated 27,102 total units Can these data be broken down by specific types of building categories? Yes Can the data on area be broken down by age of the building stock? Yes
Percentage of area of Whole (%) in different age classes Area (%) Percentage of area / occupancy Occupancy Notes / < 1900 1900-1950 1951 - 2000 >2000 attributable to: (%) Sources of data Total residential Single-family residential Multi-family residential 100 50 50 100 City Hall Kuala Other residential Lumpur Total 100 100 100
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Appendix 5.1b UNEP-SBCI Common Carbon Metric Top-Down Approach – Electricity Consumption of the Building Stock STEP 4
Enter data on electricity used by the Whole
*Disaggregated data from Energy Commission (2010) Statistics, Total of No TNB, SESB, SESCO 20,847 GWh x 0.61% of Are electricity consumption data available on a monthly basis? 27,102 *Building Stock Gigawatt hour (GWh) My electricity use data are measured in :
Electricity Month consumption 1
2 3 4 5 6 7
8 9 10 11 12
Total (summed across months): Toal for entire year:
What tier of accuracy best describes your data these data? Calculated Can these data be broken down by specific types of building categories? Yes Do you wish to use a custom emission factor? Yes Value of custom emission factor (kg GHG / kWh): 0.6190755 Value of default emisison factor (kg GHG / kWh): Total emissions (metric tonnes GHG): 0.000
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Appendix 5.1c UNEP-SBCI Common Carbon Metric Top-Down Approach – Fuel Consumption STEP 5 Enter data on fuel consumed by the Whole
Can fuel consumption data be broken down by specific types of building Yes categories?
Fuel emission factor Use default Performance Notes / (metric tonnes Fuel Used? Amount Units emission Sources of Total GHG Energy factor? Default Custom data emissions consumpti Residual fuel oil (metric on (KWh) Natural gas Diesel oil Liquified Petroleum Gases Kerosene Gasoline Anthracite coal Brown coal briquettes Coke oven coke
Fossilfuels Lignite Lignite coke Other bituminous coal Patent fuel Petroleum coke Sub bituminous coal Municipal waste (Non biomass fraction) Wood or Wood waste Other primary solid biomass fuels Charcoal Biogasoline Biodiesels Other liquid biofuels Landfill gas
Biomass fuels Biomass Sludge gas Other biogas Municipal wastes (Biomass fraction) Peat
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Appendix 5.1d UNEP-SBCI Common Carbon Metric Top-Down Approach – Fuel Consumption STEP 6
What percentage of these fuels was used in the different building categories?
Residential
Single- Multi- Fuel Total Other family family residential residential residential residential Residual fuel oil Natural gas Diesel oil Liquified Petroleum Gases Kerosene Gasoline Anthracite coal Brown coal briquettes Coke oven coke
Fossilfuels Lignite Lignite coke Other bituminous coal Patent fuel Petroleum coke Sub bituminous coal Municipal waste (Non biomass fraction) Wood or Wood waste Other primary solid biomass fuels Charcoal Biogasoline Biodiesels Other liquid biofuels Landfill gas
Biomass fuels Biomass Sludge gas Other biogas Municipal wastes (Biomass fraction) Peat
Allocated GHG emissions (tonnes CO2e.) Allocated energy consumption (kWh)
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Appendix 5.1e UNEP-SBCI Common Carbon Metric Top-Down Approach – Combined Emissions and Energy Consumption
Method 1. Performance of the Whole. Results summary. Combined emissions and energy consumption data:
Energy use (kWh) GHG emissions (metric tonnes CO2e.) Area (m2) Occupants Electricity Fuel use Total Electricity Fuel use Total
Total residential 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Single-family residential 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Multi-family residential 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Other residential 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Total (Based on data on the Whole)1 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
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Appendix 5.1f UNEP-SBCI Common Carbon Metric Top-Down Approach – Performance Metrics
Performance metrics:
By area By occupant Energy-based performance matrics, normalized by climate
2 kWh / occupant / kg CO2e. / kWh / m / yr / kWh / occupant / kWh / occupant / kWh / m2/ yr kg CO e./ m2 / yr kWh / m2/ yr / HDD 2 yr occupant / yr CDD yr / CDD yr / HDD
Total residential Single-family residential Multi-family residential Other residential Total (Based on data on the Whole)
Data have been provided for electricity and fuel consumption, as well as area / occupancy Data on fuel or electricity consumption have not been provided Data are missing and performance metrics cannot be determined
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Appendix 5.1g UNEP-SBCI Common Carbon Metric Top-Down Approach – Performance Graph
kWh / m2 / yr
Total (Based on data on the Whole) Other non-residential
Mixed-use building units Other institutional Institutional lodging Public order and safety Health care
Public assembly Education Other commercial Entertainment Food service Mercantile & service Building categoryBuilding Warehouse & storage Hotel Office Total non-residential Other residential Multi-family residential Single-family residential Total residential
0 2 4 6 8 10 12 14 16 18 20 Value of metric
Performance Metric of the *Building Stock (UNEP-SBCI, 2010)
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Appendix 5.2a UNEP-SBCI Common Carbon Metric Bottom-Up Approach –Data Characteristics
1. Characterise the individual building(s) (STEP 1) Are these buildings distributed across more than one region (e.g., city or country)? (Yes/No) Building name Building category Year of construction Address Number of occupants Area (m2) Notes/Sources of data
2. Enter data on energy use by these buildings (STEP 2) i) Purchased electricity Amount Units (kWh or MWh) ii) Burnt fuel Biomass or fossil? Specific fuel (natural gas/ diesel oil/ liquefied petroleum gases/ kerosene/ gasoline/ anthracite coal/ other bitumen briquettes/ coke oven coke/ lignite/ lignite coke/ other bituminous coal/ patent fuel/ petroleum coke/ sub bituminous coal) Amount Units (TJ/ GJ/ MJ/ kWh/ MWh/ GWh/ mmBtu/ thm/ pounds-lb/ kilogram-kg/ metric tonne/ short ton) Conversion factor to convert to TJ fuel used Emission factor (tonnes CO2e./unit)
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Appendix 5.2b UNEP-SBCI Common Carbon Metric Bottom-Up Approach – Building Characteristics (STEPs 1-3)
Characterise the individual building(s) Are these buildings distributed across more than one region (e.g., city or country)?No
Year of Construction Year of last major Number of Notes/ Source of Building name Building ID Building category (XXXX) retrofit (XXXX) Street Street number occupants Area (m2) data
0 0
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Appendix 5.2c UNEP-SBCI Common Carbon Metric Bottom-Up Approach – Electricity Consumption of Individual Households (STEP 4)
Monthly Electricity Consumption (kWh) Monthly Month/ Unit Total Level 1st 2nd 3rd 4th 5th 6th 7th 8th 9th 10th 11th 12th 13th 14th 15th 16th 17th (kWh)
Month 1 Month 2 Month 3 Month 4 Month 5 Month 6 Month 7 Month 8 Month 9 Month 10 Month 11 Month 12 Total of Year (kWh)
Average (kWh)
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Appendix 5.2d UNEP-SBCI Common Carbon Metric Bottom-Up Approach – Electricity Consumption for the PPR Low-Cost Housing Project (STEP 4)
PPR Berigin Monthly Electricity Consumption (kWh)
BLOCK A1 A2 B1 B2 C1 C2 D1 D2 E1 E2 F1 F2 TOTAL
Total of Year (kWh)
Average (kWh)
No. of Units
Tabulation of Electricity Consumption for 12 Blocks in each PPR Housing Project
Area of Area Building Units Total Monthly Electricity Building name Address Constructed each Unit Surveyed category Surveyed Consumption (kWh) (m2) (m2) Residential: Block A1 to Other known PPR Beringin 1999 63 181 category F2 Residential: Block A1 to PPR Intan Other known 2000 63 202 Baiduri category F2 383
Combined Sum for Each PPR Low-Cost Housing Projects
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Appendix 5.2e UNEP-SBCI Common Carbon Metric Bottom-Up Approach – Electricity Consumption (STEP 5)
Enter data on electricity used by these buildings Can electricity consumption data be disaggregated by month Default emission factor (kg GHG / kWh): 0.6190755
Grid electricity Green power /
Amount of renewable energy that is Custom generated emission on-site Total GHG factor (kg Amount of and emissions Use default GHG / per green transferre (metric Notes / Building emission selected power d to the tonnes Source of
Building name ID factor? Amount Units unit) purchased grid CO2e.) data Averaged PPR Beringin I through PPR Intan Baiduri T Sample Size of 383 units 0.000
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Appendix 5.2f UNEP-SBCI Common Carbon Metric Bottom-Up Approach – Emission and Electricity Consumption Summary Method 2. Bottom up approach. Summary
Individual performance metrics
These tables compare the performance of individual buildings with that of all buildings sampled from the same building category (e.g., a hotel would be compared with all hotels within the sample).
2 2 kWh / m / yr kg CO2e. / m / yr kWh / occupant / yr kg CO2e. / occupant / yr Building name Building ID This building All This building All This building All This building All
Example of Bottom-Up Individual Building Performance Metrics
Performance benchmarked against the Whole These tables compare the performance of the entire sample with that of the Whole.
2 2 kWh / m / yr kg CO2e. / m / yr kWh / occupant / yr kg CO2e. / occupant / yr Sample Whole Sample Whole Sample Whole Sample Whole Total residential kWh / m2/ yr / CDD kWh / m2/ yr / HDD kWh / occupant / yr / CDD kWh / occupant / yr / HDD Single-family residential Building name Building ID This building All This building All This building All This building All Multi-family residential Other residential Example of Bottom-Up Performance Benchmarked against the Whole (or *Building Stock)
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Appendix 5.4a Questionnaire Sample – Basic Demographics
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Appendix 5.4b Questionnaire Sample – Electricity Consumption
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Appendix 5.4c Questionnaire Sample – Summary of Electricity Consumption for both PPR Low-Cost Housing Projects
Types of Average Daily Consumption (Hours) Total Electrical Above 24 (%) Appliances (%) N/A 0 -1 1 - 2 2 - 3 3 - 4 4 - 5 5 - 6 6 hours
Artificial Lighting
Artificial Cooling
Hot Water
System
Refrigeration
Entertainment
Technology Cooking &
Kitchen Ware Clothes Washing (Washing Machine)
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Appendix 5.4d Questionnaire Sample – Household Income and Expenditure
Electricity Consumption Pattern and Affordability of Electricity This questionnaire is a part of a Doctoral (PhD) Research in Investigating Electricity Consumption Pattern and Affordability of Electricity
Household Income and Affordability Average monthly income Please mark ( X ) your answers according to the columns available
How many persons contribute to the total household income? 1 2 3 4 5 6 more than 6
Female Male
Between Between Between Between Between Below Above RM1500 to RM2000 to RM2500 to RM3000 to RM3500 to RM1500* RM4000* RM 1999 RM2499 RM2999 RM3499 RM3999
How much is the total monthly household income?
*Other amount? Please specify
Average rent or housing loan expenditure
Please mark ( X ) your answers according to the columns available
Between Between Between Between Between Below Above RM150 to RM200 to RM250 to RM300 to RM350 to RM150* RM400* RM199 RM249 RM399 RM349 RM399
How much is the average rent or housing loan repayment?
*Other amount? Please specify
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Appendix 5.4e Questionnaire Sample – Electricity and Other Utilities Expenditure
Average monthly electricity bill Please mark (X ) your answers according to the columns available
Between Between Between Between Between Below Above RM150 to RM200 to RM250 to RM300 to RM350 to RM150* RM400* RM199 RM249 RM399 RM349 RM399
How much is the average monthly electricity bill?
*Other amount? Please specify
Average monthly amenity bills (water, telephone, internet, etc)
Please mark (X ) your answers according to the columns available
Between Between Between Between Between Below Above RM150 to RM200 to RM250 to RM300 to RM350 to RM150* RM400* RM199 RM249 RM399 RM349 RM399
How much is the average monthly electricity bill?
*Other amount? Please specify
*Note: Exchange Rate (Approximate). XE.com (as of 14 June 2011)
RM 150 = AUD 47 USD 49 RM 200 = AUD 62 USD 66 RM 250 = AUD 78 USD 82 RM 300 = AUD 97 USD 94 RM 350 = AUD 114 USD 110 RM 400 = AUD 130 USD 125
RM 1500 = AUD 465 USD 494 RM 2000 = AUD 620 USD 659 RM 2500 = AUD 775 USD 824 RM 3000 = AUD 973 USD 940 RM 3500 = AUD 1135 USD 1097 RM 4000 = AUD 1298 USD 1254
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Appendix 6.1a STEPS 1-3: CCM Top-Down Approach (UNEP-SBCI, 2011)
Characterise the Whole
2 What is the total area of the Whole, in m ? 1,707,426 *Whole is the combined total of 24 PPR Housing of 27,102 units X What tier of accuracy best describes your data on area? Calculated 63m2 (m2) What is the total occupancy of the whole, in number of occupants? 135,510 *Estimated 5 persons per unit, What tier of accuracy best describes your data on occupancy? Calculated with 27,102 total units Can these data be broken down by specific types of building categories? Yes Can the data on area be broken down by age of the building stock? Yes
Percentage of area of Whole (%) in different age classes Area (%) Percentage of area / occupancy Occupancy Notes / < 1900 1900-1950 1951 - 2000 >2000 attributable to: (%) Sources of Total residential data Single-family residential City Hall Multi-family residential 100 50 50 100 Kuala Other residential Lumpur Total 100 100 100
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Appendix 6.1b STEP 2: Total Existing Residential Property for 2010 (NAPIC, 2012)
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Appendix 6.1c STEP 4a: CCM Top-Down Approach (UNEP-SBCI, 2011) Enter data on electricity used by the Whole
*Disaggregated data from Energy Commission (2010) No Statistics, Total of TNB, SESB, SESCO 20,847 GWh x Are electricity consumption data available on a monthly basis? 0.61% of 27,102 *Building Stock My electricity use data are measured in : Gigawatt hour (GWh)
Electricity Month consumption
1 2 3 4 5 6 7 8 9 10 11 12
Total (summed across months): Toal for entire year: 137.42
What tier of accuracy best describes your data these data? Calculated 296 Appendix Can these data be broken down by specific types of building categories? Yes Do you wish to use a custom emission factor? Yes Value of custom emission factor (kg GHG / kWh): 0.6190755 Value of default emisison factor (kg GHG / kWh): Total emissions (metric tonnes GHG): 85073.355
Allocated Allocated Notes / Percentage of electricity use attributable Percentage energy emissions Sources of to: use (%) consumptio (tonnes data n (TJ) CO2e.) Total residential 0.000 0.000 Single-family residential 0.000 0.000 Multi-family residential 100 494.712 85073.355 Other residential 0.000 0.000 Total 100 Enter data on electricity used by the Whole
*Disaggregated data from Energy Commission (2010) No Statistics, Total of TNB, SESB, SESCO 20,847 GWh x Are electricity consumption data available on a monthly basis? 0.61% of 27,102 *Building Stock My electricity use data are measured in : Gigawatt hour (GWh)
Electricity Month consumption
1 2 3 4 5 6 7 8 9 10 11 12 Measuring Electricity-Related GHG Emissions and Affordability of Electricity in Malaysian Low-Cost Nov 2013 Housing. Case Study of Low-Cost Housing Projects in Kuala Lumpur Total (summed across months):
AppendixToal for 6.1d entire STEPyear: 4b: CCM Top-Down Approach137.42 (UNEP-SBCI, 2011)
What tier of accuracy best describes your data these data? Calculated Can these data be broken down by specific types of building categories? Yes Do you wish to use a custom emission factor? Yes Value of custom emission factor (kg GHG / kWh): 0.6190755 Value of default emisison factor (kg GHG / kWh): Total emissions (metric tonnes GHG): 85073.355
Allocated Allocated Notes / Percentage of electricity use attributable Percentage energy emissions Sources of to: use (%) consumptio (tonnes data n (TJ) CO2e.) Total residential 0.000 0.000 Single-family residential 0.000 0.000 Multi-family residential 100 494.712 85073.355 Other residential 0.000 0.000 Total 100
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Appendix 6.1e STEP 5a: CCM Top-Down Approach (UNEP-SBCI, 2011)
Enter data on fuel consumed by the Whole Yes *Fuel type for generation mix for 2010 Can fuel consumption data be broken down by specific types of building categories? Fuel emission factor (metric tonnes Performance Use GHG/selected unit) Notes / default Fuel Used? Amount Units Total GHG Sources of emission Energy emissions data factor? Default Custom consumpti (metric on (KWh) tonnes) Gigawatt Energy Residual fuel oil Yes 0.03 hour Yes Commission Gigawatt (2010) - Natural gas Yes 110.86 hour Yes MEIH Gigawatt Diesel oil Yes 0.59 hour Yes Liquified Petroleum Gases No Kerosene No Gasoline No Anthracite coal No Brown coal briquettes No Fossilfuels Coke oven coke No Lignite No Lignite coke No Other bituminous coal No Patent fuel No Petroleum coke No Sub bituminous coal No Municipal waste (Non biomass fraction) No Wood or Wood waste No Other primary solid biomass fuels No Charcoal No Biogasoline No Biodiesels No Other liquid biofuels No Landfill gas No
Biomass fuels Biomass Sludge gas No Other biogas No Municipal wastes (Biomass fraction) No Peat No 0.000 0.000 298 Appendix
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Appendix 6.1f STEP 5b: CCM Top-Down Approach (UNEP-SBCI, 2011)
What percentage of these fuels was used in the different building categories?
Residential Total percentage Fuel Total Single-family Multi-family Other (%) allocated residential residential residential residential
Residual fuel oil 0.02 0.02 Natural gas 80.7 80.7 Diesel oil 0.4 0.4 Liquified Petroleum Gases Kerosene Gasoline Anthracite coal Brown coal briquettes Coke oven coke
Fossilfuels Lignite Lignite coke Other bituminous coal Patent fuel Petroleum coke Sub bituminous coal Municipal waste (Non biomass fraction) Wood or Wood waste Other primary solid biomass fuels Charcoal Biogasoline Biodiesels Other liquid biofuels Landfill gas
Biomass fuels Biomass Sludge gas Other biogas Municipal wastes (Biomass fraction) Peat
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Appendix 6.1g CCM Top-Down Approach Performance Metrics Summary (UNEP-SBCI, 2011)
Method 1. Performance of the Whole. Results summary. Combined emissions and energy consumption data:
Energy use (kWh) GHG emissions (metric tonnes CO2e.) Area (m2) Occupants Electricity Fuel use Total Electricity Fuel use Total Total residential 137420000.00 89466386.00 226886386.00 85073.36 18070.43 103143.78 0.00 0.00 Single-family residential 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Multi-family residential 137420000.00 89466386.00 226886386.00 85073.36 18070.43 103143.78 1707426.00 135510.00 Other residential 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Total (Based on data on the Whole)1 137420000.00 0.00 137420000.00 85073.36 0.00 85073.36 1707426.00 135510.00
Performance metrics:
By area By occupant
2 2 kWh / m / yr kg CO2e./ m / yr kWh / occupant / yr kg CO2e. / occupant / yr
Total residential Single-family residential Multi-family residential 132.88 60.41 1674.31 761.15 Other residential Total non-residential Total (Based on data on the Whole) 80.48 49.83 1014.09 627.80
Data have been provided for electricity and fuel consumption, as well as area / occupancy Data on fuel or electricity consumption have not been provided Data are missing and performance metrics cannot be determined
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Appendix 6.1h CCM Top-Down Approach Performance Metrics Graph (UNEP-SBCI, 2011)
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Appendix 6.2a STEP 1-3: CCM Bottom-Up Approach Performance (UNEP-SBCI, 2011)
Characterise the individual building(s) Are these buildings distributed across more than one Yes region (e.g., city or country)?
Year of last Year of major Building Building Construction retrofit Street Number of Notes/ Source Building name ID category (XXXX) (XXXX) Street number occupants Area (m2) of data *Unit size of 63m2 X Multi-family City Hall Kuala number of PPR Beringin I residential 1999 Jln Miri 1027 905 11,403 Lumpur units of *Unit size of 63m2 X Multi-family Jln Tmn City Hall Kuala number of PPR Intan Baiduri T residential 2000 Intan Baiduri 77 1,010 12,726 Lumpur units of
1,915 24,129
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Appendix 6.2b STEP 4: CCM Bottom-Up Approach Performance (UNEP-SBCI, 2011)
Enter data on electricity used by these buildings
Can electricity consumption data be disaggregated by month Yes Default emission factor (kg GHG / kWh): 0.6190755
Green power / Grid electricity renewable energy Amount of Custom renewable emission energy Total factor that is GHG (kg GHG Amount generated emissions Use default / per of green on-site and (metric Notes / emission selected power transferred tonnes Source
Building name Building ID factor? Amount Units unit) purchased to the grid CO2e.) of data Kilowatt Averaged PPR Beringin I Yes 480,616.00 hour (kWh) 297.006 through Kilowatt Sample PPR Intan Baiduri T Yes 513,065.00 hour (kWh) 317.059 Size of 383 units 614.065
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Appendix 6.2c STEP 5a: CCM Bottom-Up Approach Performance (UNEP-SBCI, 2011)
Enter data on fuel used by these buildings (if more than one fuel is used in any given building, please use additional tables)
First Fuel
Emission factor (metric tonnes GHG/selected unit) Performance
Total GHG Use default emissions Energy Notes/ Building emission (metric consumption Source of Building name ID Fuel Amount Units factor? Default Custom tonnes) (KWh) data
Kilowatt MEIH PPR Beringin I Natural gas 387711 hour (kWh) Yes 0.000 78.309 387711.000 Database PPR Intan Kilowatt Baiduri T Natural gas 413889 hour (kWh) Yes 0.000 83.597 413889.000
161.906 801600.000
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Appendix 6.2d STEP 5b: CCM Bottom-Up Approach Performance (UNEP-SBCI, 2011)
Second Fuel
Emission factor (metric tonnes GHG/selected unit) Performance Use Total GHG default emissions Energy Notes/ Building emission (metric consumption Source of Building name ID Fuel Amount Units factor? Default Custom tonnes) (KWh) data Residual fuel Kilowatt MEIH PPR Beringin I oil 92.12 hour (kWh) Yes 0.000 0.026 92.120 Database PPR Intan Residual fuel Kilowatt Baiduri T oil 102.6 hour (kWh) Yes 0.000 0.029 102.600
0.054 194.720
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Appendix 6.2e STEP 5c: CCM Bottom-Up Approach Performance (UNEP-SBCI, 2011)
Third Fuel
Emission factor (metric tonnes GHG/selected unit) Performance Use Total GHG default emissions Energy Notes/ Building emission (metric consumption Source of Building name ID Fuel Amount Units factor Default Custom tonnes) (KWh) data Kilowatt hour MEIH PPR Beringin I Diesel oil 2066.6 (kWh) Yes 0.000266798 0.551 2066.600 Database Kilowatt PPR Intan hour Baiduri T Diesel oil 2206.2 (kWh) Yes 0.000266798 0.589 2206.200
1.140 4272.800
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Appendix 7.1a Average Consumption of Electrical Appliances for PPR Beringin
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Appendix 7.1b Average Consumption of Electrical Appliances for PPR Intan Baiduri
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Appendix 7.2a Average Monthly Household Income for PPR Beringin
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Appendix 7.2b Average Monthly Household Income for PPR Intan Baiduri
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Appendix 7.3a Average Monthly Rent/Housing Loan Expenditure for PPR Beringin
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Appendix 7.3b Average Monthly Rent/Housing Loan Expenditure for PPR Intan Baiduri
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Appendix 7.4a Average Monthly Electricity Bill Expenditure for PPR Beringin
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Appendix 7.4b Average Monthly Electricity Bill Expenditure for PPR Intan Baiduri
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Appendix 7.5a Average Monthly Utility Expenditure for PPR Beringin
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Appendix 7.5b Average Monthly Utility Expenditure for PPR Intan Baiduri
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